Part 1 – Survey

Introduction to Web Data Mining:

The term Web Data Mining is a procedure used to creep through different web resources to gather required information, which empowers an individual or an organization to advance business, comprehension showcasing elements, new advancements skimming on the Internet, and so forth. There is a developing pattern among organizations, associations and people alike to accumulate information through web data mining to use that information to their greatest advantage.

Because of the heterogeneity and absence of structure of Web data, mechanized revelation of focused or unforeseen learning/information is a testing undertaking. It calls for novel techniques that draw from an extensive variety of fields crossing data mining, machine learning, common dialect handling, insights, databases, and information recovery. In the previous couple of years, there was a quick extension of exercises in the Web mining field, which comprises of Web use mining, Web structure mining, and Web content mining. Web utilization mining alludes to the revelation of client access designs from Web use logs. Web structure mining tries to find valuable information from the structure of hyperlinks. Web content mining plans to concentrate/mine valuable information or learning from Web page contents. For this exceptional issue, we concentrate on Web content mining [1].

Data Mining is done through different sorts of data mining software. These can be basic data mining software or very particular for point by point and broad errands that will be filtering through more information to choose better bits of information. For instance, if an organization is searching for information on specialists including their messages, fax, phone, area, and so forth. This information can be mined through one of these data mining software programs. This information accumulation through data mining has permitted organizations to make thousands and a large number of dollars in incomes by having the capacity to better utilize the web to pick up business knowledge that assists organizations with settling on key business decisions.

Types of Web Mining:

The Web mining can be classified into the following 3 Categories,

1. Web usage mining,

  1. Web content mining
  2. Web Structure mining

Web Usage Mining

With the proceeded with development and expansion of e-trade, Web administrations, and Web-based information systems, the volumes of clickstream, exchange data, and client profile data gathered by Web-based associations in their day by day operations has come to galactic extents. Breaking down such data can help these associations focus the life-time estimation of customers, configuration cross-advertising systems crosswise over items and administrations, assess the adequacy of limited time crusades, streamline the usefulness of Web-based applications, give more customized content to guests, and locate the best intelligent structure for their Web space. This sort of investigation includes the programmed revelation of significant examples and connections from a huge accumulation of basically semi-organized data, regularly put away in Web and applications server access logs, and additionally in related operational data sources.

A vital undertaking in any data mining application is the making of a suitable target data set to which data mining and factual calculations can be connected. This is especially critical in Web use mining because of the qualities of clickstream data and its relationship to other related data gathered from various sources and over different channels. The data readiness procedure is regularly the most tedious and computationally serious stride in the Web use mining procedure, and frequently requires the utilization of exceptional calculations and heuristics not normally utilized in different spaces. This procedure is basic to the fruitful extraction of valuable examples from the data. The procedure may include pre-preparing the first data, coordinating data from various sources, and changing the incorporated data into a structure suitable for info into particular data mining operations. On the whole, we allude to this procedure as data arrangement.

 

Benefits of Web Usage Mining:

Web utilization mining basically has numerous favorable circumstances which makes this innovation appealing to organizations including the administration offices. This innovation has empowered e-business to do customized advertising, which in the long run results in higher exchange volumes. Government offices are utilizing this innovation to arrange dangers and battle against terrorism. The anticipating ability of identifying so as to mine applications can advantage society criminal exercises. The organizations can build up better client relationship by giving them precisely what they require. Organizations can comprehend the client’s needs better and they can respond to client needs speedier. The organizations can discover, draw in and hold clients; they can save money on creation costs by using the procured knowledge of client necessities. They can expand productivity by target estimating in light of the profiles made. They can even locate the client who may default to a contender the organization will attempt to hold the client by giving limited time offers to the particular client, consequently decreasing the danger of losing a client or clients [2].

Issues in Web Usage mining:

Web use mining independent from anyone else does not make issues, but rather this innovation when utilized on data of individual nature may bring about concerns. The most condemned moral issue including web use mining is the intrusion of Privacy is viewed as lost when information concerning an individual is acquired, utilized, or scattered, particularly if this happens without their insight or assent. The acquired data will be dissected, and grouped to shape profiles; the data will be made unknown before bunching with the goal that there are no individual profiles. In this manner these applications de-individualize the clients by passing judgment on them by their mouse clicks. De-individualization, can be characterized as a propensity of judging and treating individuals on the premise of gathering qualities rather than all alone individual attributes and merits. Another imperative concern is that the organizations gathering the data for a particular reason may utilize the data for an entirely unexpected reason, and this basically damages the client’s advantage.

The developing pattern of offering individual data as an item urges website proprietors to exchange individual data acquired from their webpage. This pattern has expanded the measure of data being caught and exchanged expanding the likeliness of one’s security being attacked. The organizations which purchase the data are obliged make it mysterious and these organizations are considered creators of any particular arrival of mining examples. They are legitimately in charge of the discharge’s contents; any errors in the discharge will bring about genuine claims, however there is no law keeping them from exchanging the data [3].

Some mining calculations may utilize questionable qualities like sex, race, religion, or sexual introduction to sort people. These practices may be against the counter separation enactment. The applications make it difficult to distinguish the utilization of such disputable qualities, and there is no solid standard against the use of such calculations with such properties. This procedure could bring about refusal of administration or a benefit to an individual taking into account his race, religion or sexual introduction, at this time this circumstance can be stayed away from by the high moral gauges kept up by the data mining organization. The gathered data is being made unknown so that, the acquired data and the got designs can’t be followed back to a person. It may look as though this represents no danger to one’s protection, however extra information can be induced by the application by joining two separate deceitful data from the client.

 Web Content Mining:

Web substance mining is the procedure of extricating information from the substance, commonly the content of reports or their portrayals. Web structure mining is the procedure of surmising information from the World Wide Web association and connections in the middle of references and referents in the web. It examines the site’s hyperlink and archive structure. At last, web use mining, otherwise called web log mining, dissects client conduct on the web webpage by extricating intriguing examples from the web server logs.

It is a system for comprehension client conduct as it identifies with the utilization of websites. The consequences of web mining can be utilized to give measurements on the adequacy of an organization’s web website or the accomplishment of a specific battle.

As of late, software sellers and scientists have been concentrating on utilizing the removed examples from web emulating to anticipate the following client solicitation amid an online session with a web website, particularly e-business. Such systems are called recommender systems and are helpful apparatuses to anticipate client demands [4].

Analysis of Sequential and Navigational Patterns

The method of consecutive example mining endeavors to discover between session examples such that the vicinity of an arrangement of things is trailed by another thing in a period requested arrangement of sessions or scenes. By utilizing this methodology, Web advertisers can anticipate future visit designs which will be useful in putting commercials went for certain client bunches. Different sorts of worldly examination that can be performed on successive examples incorporate pattern investigation, change point discovery, or closeness examination. In the setting of Web use data, successive example mining can be utilized to catch continuous navigational ways among client trails.

Successive examples (SPs) in Web use data catch the Web page trails that are regularly gone to by clients, in the request that they were gone by. In the connection of Web use data, CSPs can be utilized to catch continuous navigational ways among client trails. Interestingly, things showing up in SPs, while protecting the fundamental requesting, need not be neighboring, and consequently they speak to more broad navigational examples inside of the site. The perspective of Web exchanges as groupings of site visits takes into account various valuable and all around considered models to be utilized as a part of finding or breaking down client route designs. One such approach is to show the navigational exercises in the Web webpage as a Markov model: every online visit( (or a classification) can be spoke to as a state and the move likelihood between two states can speak to the probability that a client will explore from one state to the next. This representation takes into account the calculation of various valuable client or site measurements. For instance, one may process the likelihood that a client will make a buy, given that she has performed an inquiry in an online list. Markov models have been proposed as the fundamental displaying hardware for connection forecast and also for Web prefetching to minimize system latencies. The objective of such methodologies is to anticipate the following client activity in view of a client’s past surfing conduct. They have additionally been utilized to find high likelihood client navigational trails in a Web webpage. More modern measurable learning procedures, for example, blends of Markov models, have additionally been utilized to bunch navigational groupings and perform exploratory examination of clients’ navigational conduct in a site [5].

Opportunities with advanced analytics

This section identifies and discusses some of the opportunities the business managers dealing with the aforementioned mining technologies face for gaining competitive advantages for their businesses.

Data mining

Data mining is essentially utilized for upper hands by organizations with an in number customer core interest. The center of data mining applications amongst the business pioneers has been consistently developing from client examination to relationship investigation.

Progressively focused business and buyer commercial centers make it basic for organizations to draw in clients, as well as to hold them particularly that little rate of exceedingly productive clients. Maintenance techniques for esteemed clients by and large concentrate on monetary and/or administration level motivators to advance dedication. Since just few organizations can appreciate the economies of scale (or speculation capital) to maintain aggressive separation on cost alone, numerous businesses try to amplify client esteem by building faithfulness through brand and administration separation. This methodology put a premium on the nature of each client contact as every connection serves to either assemble mark or pulverize it.

These business pioneers use progressed investigative with data mining to streamline their client connections. Illustrations include: enhancing the adequacy of advertising effort and drawing in new clients, amplifying the estimation of offers to existing clients (cross-offering and up-offering), minimizing client [6].

Web Usage Mining as Tool for Personalization

The substance of personalization is the flexibility of information systems to the needs of their clients. This issue is turning out to be progressively critical on the Web, as non-master clients are over whelmed by the amount of information accessible on the web, while business Websites endeavor to increase the value of their administrations so as to make faithful associations with their guests clients. By evaluating Web personalization through the crystal of personalization arrangements received by Websites and actualizing an assortment of capacities. In this setting, the territory of Web use mining is a significant wellspring of thoughts and techniques for the usage of personalization usefulness.

Early work in Web use mining did not consider widely its utilization for personalization. Its essential center was on the disclosure of choice bolster learning, communicated regarding expressive data models to be assessed and abused by human specialists. All that is required for the utilization of Web use mining to Web personalization is a movement of center from the customary, choice bolster information disclosure, i.e., the static displaying of use data, to the revelation of operational learning for personalization, i.e., the dynamic demonstrating of clients. This sort of learning can be specifically conveyed back to the clients keeping in mind the end goal to enhance their involvement in the site, without the intercession of any human master. Along these lines, it is presently widely perceived that utilization mining is a significant wellspring of thoughts and answers for Web personalization. Taking into account this perspective of the Web use mining procedure, study of late work for examination in Web personalization. Beginning with an examination of the Web personalization idea and its connection to Web utilization mining, the accentuation thusly, is on the approach embraced in Web use mining, the different arrangements that have been exhibited in the writing and the route in which these routines can be connected to Web personalization systems. In perusing the study, it ought to be remembered that Web utilization mining is not a full grown hunt region. Thus, the overview addresses additionally numerous open issues, both at a specialized and at a methodological level [1].

Issues

The principle issue with content-based is the trouble of investigating the content of Web pages and touching base at semantic likenesses. Regardless of the fact that one overlooks sight and sound content, characteristic dialect itself is a rich and unstructured wellspring of data. In spite of the noteworthy procedure accomplished in the exploration handle that arrangement with the examination of printed data, we are still a long way from getting a machine to comprehend normal dialect the way people do. Content based sifting receives an assortment of factual systems for the extraction of helpful information from literary data. In any case, the examination’s issue of Web content still remains and turns out to be considerably more basic when there is constrained literary content. By diminishing the accentuation on Web content, communitarian sifting addresses this imperative issue. Besides, community sifting strategies encourage the misuse of utilization examples that are not kept to strict semantic limits [7].

 

 

 

Part 2 – Approach towards the Solutions

 

Introduction

New network technologies have opened the way for new distributed database architectures and protocols no longer built around a bottleneck in network communications. Current systems are optimized to minimize network communication due to historical bandwidth limitations as well as network communications being the major bottleneck of any distributed system in general.

In this section, we seek to explore the effect of new network technologies on distributed database systems and how distributed databases may be changed with this bottleneck no longer an issue. From the survey completed on this problem it is clear that multiple challenges and solutions exist.

The solution to the overall problem can be broken down to addressing each component individually. The overall solution in terms of the database itself is heavily based on the concepts of database replication & migration. In summary, we now have the ability to quickly send large amounts of data reliably. A DDBMS should take advantage of this and send as much data as needed without being concerned with reduction or sending fragments.

Web utilization mining has risen as the fundamental device for acknowledging more customized, user-accommodating, and business-ideal Web services. Progresses in data pre-processing, demonstrating, and mining procedures, connected to the Web data, have as of now brought about numerous fruitful applications in versatile data systems, personalization services, Web examination apparatuses, and content administration systems. As the unpredictability of Web applications and user’s collaboration with these applications expands, the requirement for smart examination of the Web use data will keep on developing. Utilization examples found through Web use mining are compelling in catching thing to-thing and user-to-user connections and similitudes at the level of user sessions. Then again, without the advantage of more profound space learning, such examples give little understanding into the hidden purposes behind which such things or users are gathered together. Besides, the inalienable and expanding heterogeneity of the Web has required Web-based applications to all the more adequately incorporate an assortment of sorts of data over numerous channels and from distinctive sources. Along these lines, an emphasis on strategies and architectures for more viable incorporation and mining of content, utilization, and structure data from distinctive sources is liable to prompt the up and coming era of more useful and more canny applications, and more refined devices that can get insight from user exchanges on the Web, not restricted to their snaps amid sessions on consistent Web locales, additionally their questions and whole associations with web search tools, and even online Ads that they experience [8].

Challenges in Web Data Mining

The Challenges in addressing the problem of Web Data Mining are as follows:

  1. How to fully Utilize Network Resources
  2. How to work better with new limits? (I.e. propagation delay)
  3. How to maintain distributed architecture?
  4. How to enhance performance by keep maintaining ACID properties?

The Role of Web Usage Mining in Personalization

Amid the most recent years, analysts have proposed another bringing together range for all strategies that apply data mining to Web data, named Web Mining. Web mining instruments expect to concentrate learning from the Web, as opposed to recovering data. Regularly, Web mining work is grouped into the accompanying three classifications Web Content Mining, Web Usage Mining and Web Structure Mining [9].

Web content mining is worried with the extraction of useful information from the content of Web pages, by utilizing data mining. Web usage mining, goes for analyzing so as to find fascinating examples of use, Web usage data. At long last, Web structure mining is another zone, worried with the utilization of data mining to the Web’s structure chart.

Web mining is a finished process as opposed to a calculation. On account of Web usage mining this procedure results in the disclosure of learning that worries the conduct of users. Initially, the point of Web usage mining has been to bolster the human choice making procedure and, in this way, the procedure’s result is commonly an arrangement of data models that uncover certain learning about data things, similar to Web pages, or items accessible at a specific Web website. These models are assessed and abused by human specialists, for example, the business sector investigator who looks for business insight, or the site manager who needs to upgrade the site’s structure and improve the scanning knowledge of visitors [1].

In spite of the way that the main part of the work in Web usage mining is not worried with personalization, its connection to robotized personalization instruments is clear. The work on Web usage mining can be a wellspring of thoughts and arrangements towards acknowledging Web personalization. Usage data, for example, those that can be gathered when a user skims a particular Web webpage, speak to the connection between the user and that specific Web website. Web usage mining gives a way to deal with the gathering and preprocessing of those data, and builds models speaking to the conduct and the hobbies of users.

These models can be used by a personalization system naturally, i.e., without the mediation of any human master, for understanding the required personalization capacities. This kind of information, i.e., the user models, constitutes operational learning for Web personalization. Subsequently, a Web personalization system can utilize Web usage mining routines with a specific end goal to accomplish the required power and adaptability [10]. The nearby connection between Web usage mining and Web personalization is the principle inspiration for this review. Considering its use for Web personalization, and being basically a data mining procedure, Web usage mining comprises of the essential data mining stages:

Data Collection: Amid this stage, usage data from different sources are accumulated and their content and structure is recognized. For Web usage mining, data are gathered from Web servers, from customers that interface with a server, or from go-between sources, for example, intermediary servers and bundle sniffers. Various systems that have been utilized at this stage, can be used to achieve productive gathering of user data for personalization. Data Preprocessing. This is the phase where data are cleaned from commotion, their irregularities are determined, and they are coordinated and solidified, keeping in mind the end goal to be used as information to the following phase of Pattern Discovery. In Web usage mining, this includes basically data separating, user recognizable proof and user session ID. The systems that are used here can give effective data elaboration.

Pattern Discovery: In this stage, information is found by applying machine learning and factual procedures, for example, bunching, characterization, affiliation revelation, and consecutive example disclosure to the data. The examples required for Web personalization, compare to the conduct and hobbies of users. This is the phase where the learning routines are connected keeping in mind the end goal to mechanize the development of user models.
Knowledge Post-Processing. In this last stage, the extricated information is assessed and more often than not exhibited in a frame that is justifiable to people, e.g. utilizing reports, or representation procedures. For Web personalization the extricated learning is consolidated in a Personalization module with a specific end goal to encourage the personalization capacities.

 

 

Every stage presents different troubles that are specific to Web usage mining. These issues have been tended to in numerous late works. It ought to be noted again here that Web usage mining is less develop than other application zones of data mining. Accordingly, an issues’ portion that have been concentrated on in data mining research and are considered as partitioned phases of the data mining procedure, for example, the issue definition and the assessment of the removed information are still unexplored domain in Web usage mining [1].

Data Mining Techniques

The following techniques based on the user’s usability like Web usage mining, Web content mining or Web structure mining. These techniques are designed to solve the issues, it should also be noted that a few of them may or may not be perfect but they do help us. A general comparison is also provided in the end that shows which approach one should utilize and when.

Unique Table Approach Mining

This unique or exceptional table methodology mining is prevalently known as propositional data mining. The vital errand of the data mining is known as Propositional Data Mining. The point presumption is that every individual is spoken to by an altered arrangement of qualities which is known as attributes. Again the individual can be considered as an accumulation of attribute –value sets, which are spoken to as a vector group. In this approach the focal database of people turns into a table: which comprises of lines (or tuples) compare to people and columns (attribute). In the event that we need to build up the connection among the diverse tables (which might as Primary and Foreign key connections) then we have experience the two issues. There are two approaches to handle the issue [11].

Structured Data Mining

The term structured data mining intends to handle the unpredictable data. That is this sort of data that can’t store in the sensible in one table or doesn’t have a solitary table representation in which table lines or columns are not identified with one another. Presently a day’s everybody utilizing multi-social databases i.e. databases of numerous tables. A few issues including atoms, proteins, phylogenetic trees, informal organizations and web server access logs can’t be handled if columns in a solitary table are not unequivocally connected to one another. To manage structured data a wide range of issues must be handled that are unimportantly settled in attribute-quality mining calculations. In present day’s situation, the measure of data is accessible immensely so human must take assistance from programmed electronic strategies for removing the required data. These sorts of data must be spoke to as chart data structure which will speak to the hubs and their attributes, associations with alternate elements.

 

Inductive Logic Programming (ILP)

This ILP worldview says that how the rationale project will change over the examples. The rationale project affected from a database of consistent certainties which is called as ILP.ILP takes after the top-down methodology. The upside of ILP is excessively expressive and intense principles are reasonable the disservice of ILP is Inefficient for the database with the intricate diagram and in addition not legitimately took care of the nonstop attributes. The database comprises of an accumulation of actualities in first-arrange rationale. Every actuality speaks to a section, and people can be remade by sorting out these certainties. To begin with request rationale regularly Prolog can be used to choose subgroups [3].

Graph Mining:

Chart mining is the strategies which will remove the required data from data spoke to as diagram structured structure. A diagram can be characterized as the comparison G = {V, E}, Where V = {v1,v2,v3,… … .vn} is a requested arrangement of vertices in the chart and ={e1,e2,e3,… … ..en} is the arrangement of pair of edges. The term diagram mining which can allude to find the chart designs. Analysts have characterized the two situations: First: It is the run of the mill of web areas and the second one is: database of synthetic mixes. The fundamental goal of the Graph Mining is the idea of incessant diagram design. Diagrams turns out to be progressively vital in displaying confounded structures, ,for example, circuits ,pictures, concoction mixes ,protein structures, natural systems ,informal communities ,the web ,work processes and XML records. Numerous diagrams look calculations have been produced in concoction informatics, PC vision, video indexing and content recovery. With the expanding interest on the examination of a lot of structured data, chart mining has turned into a dynamic and imperative subject hotel data mining. Chart mining is used to mine continuous diagram designs and perform portrayal, separation, grouping etc [12]

TupleID Propagation

It is a technique for exchanging data among diverse relations by essentially goes along with them. This technique display to inquiry in the social database and which is watched that less unreasonable than physical joins in both time and space. It is the method for performing virtual joins among relations which less sweeping than physical joins. When we need to inquiry a decent predicate then we will use spread Tuple Ids between any two relations which gives the less calculations and capacity expense contrasted and making join necessities and requirements.

Semi-Structured Data Mining

Presently a days the data mining is worried with the revelation of examples and in addition the relations .Almost all the data mining calculations handle the data with the settled frame however the diagram is characterized with development When the data is on the web then its structure is standard structure .We ordinarily called as semi-structured data mining .To handle such kind of data we use the XML dialect .XML handles the plain data as well as the self-assertive trees. Semi-structured data is normally demonstrated regarding charts contain names that offer semantics to the basic structure. The database comprises of XML archives, which depict objects in a blend of auxiliary and free-message data [12].

Multi-Relational Data Mining (MRDM)

The database comprises of an accumulation of tables (a social database). Records in every table speak to parts, and people can be reproduced by joining over the remote key relations between the tables. The database comprises of a gathering of tables (a social database). Records in every table Represent parts, and people can be recreated by joining over the remote key relations between the tables. Subgroups can be characterized by method for SQL or a graphical query language [12].

Comparison between Existing Method (ILP, SSDM) VS MRDM

During comparison so many attribute to be considered these are as below:

Property Inductive Logic Program (ILP) SSDM ( Semi Structured Data Mining) Multi-Relational Data Mining
Attributes Possible Yes Possible
Numeric value Possible Not Possible Possible
Intentional data Possible Not Possible Possible
Order in structural parts Not Possible Possible Not Possible
Graph/Tree Possible for Graph Representation Possible for Tree Representation Possible for Graph Representation
Structured terms Possible Not Possible Possible

In the above correlation of the ILP, SSDM and MRDM the credit goes to MRDM .Multi Relational data mining backings every one of the properties which are said above. As correlations of ILP, SSDM and MRDM, the support goes to MRDM.

Data is a vital issue. Managing deficient crude data or incorrect information is not a paltry errand. The data’s measure set expected to apply a calculation, copy data, and transient data and additionally interactive media representation of data are concerns. How a data mining method can figure out how to enhance itself through experience is another intriguing issue to consider.

Algorithms contrast in the ways the models are produced. How the nature of a calculation is evaluated, its power, versatility, preprocessing, generalizability, and unwavering quality are a percentage of the basic issues. The way display execution is measured is a vital thought. The same model performs distinctively in diverse areas because of the data’s nature, standardizing criteria, and the chief variables included. Clamor resilience and affectability investigation are likewise of premium.

Scalable is especially huge. Scalable alludes to the capacity to keep up execution as the data’s span base being mined increments. Scalable mining instruments exploit parallel figuring. Subsequently, better parallel algorithms and also direct access to parallel data base administration systems are exploration themes [4].

At the point when humans are included, choice making displaying and the database regularly need to consider subjective elements. What’s more, space errand attributes influence model execution. Non-linearity and non-monotonicity issues can be tended to by data perception.

In synopsis, in creating a data mining model and in selecting the suitable model appraisal technique, data mining research needs to join the attributes of a given assignment space, quality and arrangement of a data set that speaks to an area, the choice making environment, the human components, and potential connection among them. Figure 1 is a straightforward system that fuses these thoughts

It is clear that data mining still in its early stages and in this manner offers incredible examination opportunities. It is a multidisciplinary field, utilizing inputs from analysts who manage data, PC researchers and operations specialists making new algorithms, and data systems individuals taking a gander at how to adapt to the systemic and human impediments that exist [13].

References

[1] DIMITRIOS PIERRAKOS, GEORGIOS PALIOURAS, “Web Usage Mining as a Tool for Personalization : A Survey,” User Modeling and User-Adapted Interaction, vol. 13, pp. 311-372, 2003.
[2] “Integrating business analytics into strategicplanning for better performance,” Journal of Business Strategy, vol. 6, pp. 30-39, 2011.
[3] Lin, Jimmy, and Miles Efron, “Overview of the TREC-2013 microblog track,” in TREC vol. 2013, 2013.
[4] Ah-Hwee Tan, “Text Mining:The state of the art and the challenges,” Singapore, 2003.
[5] Bing Liu, Kevin Chen-Chuan Chang, “Editorial: Special Issue on Web Content Mining,” SIGKDD Explorations., vol. 6, no. 1, pp. 1-4, 2006.
[6] MICHAEL S. LEW, NICU SEBE, CHABANE DJERABA, RAMESHJAIN, “Content-Based Multimedia Information Retrieval: State of the Art and Challenges,” ACM Transactions on Multimedia Computing, Communications and Applications, vol. 2, no. 1, pp. 1-19, 2006.
[7] Bamshad Mobasher and Olfa Nasraoui, “Web usage mining,” Web data mining: Exploring hyperlinks, contents and usage data, p. 12, 2006.
[8] Marten Schläfke, Riccardo Silvi, Klaus Möller, “A framework for business analytics in performancemanagement,” International Journal of Productivity and Performance Management, vol. 62, no. 1, pp. 110-122, 2012.
[9] M. D. M. H. R. H. J. P. M. S. S. Eric Brewer, “The Challenges of Technology Research for Developing Regions.,” IEEE Pervasive Computing.Volume 5, Number 2, pp. 15-23, 2006.
[10] Wang K., Liu H, “Discovering structural association of semi structured data,” IEEE Trans. Knowledge and Data Engineering, vol. 12, no. 3, pp. 353-371, 2000.
[11] Ranjit Bose, “Advanced analytics: opportunities and challenges,” Industrial Management & DataSystems, vol. 109, no. 2, pp. 155 – 172, 2009.
[12] Neelamadhab Padhy, Rasmita Panigrahi, “Multi Relational Data Mining Approaches: A Data Mining Technique,” International Journal of Computer Applications, vol. 57, no. 17, 2012.
[13] H. Michael Chung and Paul Gray, “Current Issues in Data Mining,” Journal of Management Information Systems, p. forthcoming.
[14] R. Cooley, B. Mobasher, and J. Srivastava , “Web Mining: Information and Pattern Discovery on the World Wide Web,” IEEE , University of Minnesota, Minneapolis, 1997.
[15] Joe F. Hair, “Knowledge creation in marketing: the role of predictive analytics,” European BusinessReview, vol. 19, no. 4, pp. 303-315, 2007.
[16] Enrique Flores, Alberto Barr´on-Cedeno, Paolo Rosso,and Lidia Moreno, “Towards the Detection of Cross-Language Source Code Reuse,” in Natural Language Processing and Information Systems, Berlin Heidelberg, 2011.
[17] A. Clare, H. E. Williams, and N. Lester, “Scalable multi-relational association mining.,” ICDM, 2004.
[18] Hong Yu, Xiaolei Huang, Xiaorong Hu, Hengwen CAI, “A comparative study on Data mining algorithm for individual Credit risk Evaluation,” in IEEE conference 2012, 2012.

 

LEAVE A REPLY

Please enter your comment!
Please enter your name here