Extraction of Target User Group from Web Usage Data Using Evolutionary Biclustering Approach

Data mining extracts hidden information from a database that the user did not know existed. Biclustering is one of the data mining technique which helps marketing user to target marketing campaigns more accurately and to align campaigns more closely with the needs, wants, and attitudes of customers and prospects. The biclustering results can be tuned to find users’ browsing patterns relevant to current business problems. This paper presents a new application of biclustering to web usage data using a combination of heuristics and meta-heuristics algorithms. Two-way K-means clustering is used to generate the seeds from preprocessed web usage data, Greedy Heuristic is used iteratively to refine a set of seeds, which is fast but often yield local optimal solutions. In this paper, Genetic Algorithm is used as a global optimizer that can be coupled with greedy method to identify the global optimal target user groups based on their coherent browsing pattern. The performance of the proposed work is evaluated by conducting experiment on the msnbc, a clickstream dataset from UCI repository. Results show that the proposed work performs well in extracting optimal target users groups from the web usage data which can be used for focalized marketing campaigns.

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A Survey Report On Current Research and Development of Data Processing In Web Usage Data Mining

International Journal of Database Theory and Application ◽

10.14257/ijdta.2016.9.5.10 ◽

2016 ◽

Vol 9 (5) ◽

pp. 101-110 ◽

Cited By ~ 1

Author(s):

Nandita Agrawal ◽

Anand Jawdekar

Keyword(s):

Data Mining ◽

Research And Development ◽

Data Processing ◽

Web Usage ◽

Survey Report ◽

Usage Data

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Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method

Applied Computing and Informatics ◽

10.1016/j.aci.2014.10.001 ◽

2016 ◽

Vol 12 (1) ◽

pp. 90-108 ◽

Cited By ~ 114

Author(s):

D.A. Adeniyi ◽

Z. Wei ◽

Y. Yongquan

Keyword(s):

Data Mining ◽

Recommendation System ◽

Nearest Neighbor ◽

Classification Method ◽

K Nearest Neighbor ◽

Web Usage ◽

Knn Classification ◽

Usage Data

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Recommendation system with Automated Web Usage data mining by using K-Nearest Neighbour(KNN) classification and Artificial Neural Network (ANN) algorithm

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2017.8022 ◽

2017 ◽

Vol V (VIII) ◽

pp. 160-168

Author(s):

Er. Jyoti

Keyword(s):

Neural Network ◽

Data Mining ◽

Artificial Neural Network ◽

Recommendation System ◽

Nearest Neighbour ◽

Web Usage ◽

Knn Classification ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Usage Data

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Developing an intelligent trip recommender system by data mining methods

Global Journal of Information Technology Emerging Technologies ◽

10.18844/gjit.v6i1.398 ◽

2016 ◽

Vol 6 (1) ◽

Cited By ~ 1

Author(s):

Tamer Uçar

Keyword(s):

Data Mining ◽

System Framework ◽

Related Area ◽

Simple Task ◽

Related Services ◽

Search Results ◽

Target User ◽

User Groups ◽

Mining Methods ◽

Wide Usage

Internet has a very wide usage in almost every sector. People are continuously looking and searching for information through internet. Narrowing down relevant search results is not a very simple task. Recommender systems are being used in almost every search related area. Tourism domain is one of these sectors. This study proposes an implementation of an expert system framework which can accurately classify users and make predictions about user classifications for recommending tourism related services. Proposed approach predicts clusters for system users and according to these user clusters, trips, hotels and such services can be recommended individually or as a campaign to target user or user groups. Keywords: Trip recommender, data mining, expert systems

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ARCHCOLLECT FRONT-END - A Web usage data mining knowledge acquisition mechanism focused on static or dynamic contenting applications

Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems ◽

10.5220/0002638402580262 ◽

2004 ◽

Keyword(s):

Data Mining ◽

Knowledge Acquisition ◽

Web Usage ◽

Front End ◽

Usage Data

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A Discrete Artificial Bees Colony Inspired Biclustering Algorithm

International Journal of Swarm Intelligence Research ◽

10.4018/jsir.2012010102 ◽

2012 ◽

Vol 3 (1) ◽

pp. 30-42 ◽

Cited By ~ 2

Author(s):

R. Rathipriya ◽

K. Thangavel

Keyword(s):

Fitness Function ◽

Merit Function ◽

Discrete Version ◽

Binary Particle Swarm Optimization ◽

Data Mining Technique ◽

Artificial Bee Colony Optimization ◽

Web Usage ◽

Bee Colony ◽

The Web ◽

Usage Data

Biclustering methods are the potential data mining technique that has been suggested to identify local patterns in the data. Biclustering algorithms are used for mining the web usage data which can determine a group of users which are correlated under a subset of pages of a web site. Recently, many blistering methods based on meta-heuristics have been proposed. Most use the Mean Squared Residue as merit function but interesting and relevant patterns such as shifting and scaling patterns may not be detected using this measure. However, it is important to discover this type of pattern since commonly the web users can present a similar behavior although their interest levels vary in different ranges or magnitudes. In this paper a new correlation based fitness function is designed to extract shifting and scaling browsing patterns. The proposed work uses a discrete version of Artificial Bee Colony optimization algorithm for biclustering of web usage data to produce optimal biclusters (i.e., highly correlated biclusters). It’s demonstrated on real dataset and its results show that proposed approach can find significant biclusters of high quality and has better convergence performance than Binary Particle Swarm Optimization (BPSO).

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Extraction of Knowledge from Web Server Logs Using Web Usage Mining

Asian Journal of Computer Science and Technology ◽

10.51983/ajcst-2019.8.s3.2113 ◽

2019 ◽

Vol 8 (S3) ◽

pp. 12-15

Author(s):

B. Harika ◽

T. Sudha

Keyword(s):

Data Mining ◽

Web Mining ◽

Web Server ◽

Primary Source ◽

Web Usage Mining ◽

Data Mining Techniques ◽

Web Usage ◽

Browsing Behavior ◽

The Web ◽

Usage Data

Information on internet increases rapidly from day to day and the usage of the web also increases, thus there is the need to discover interesting patterns from web. The process used to extract and mine useful information from web documents by using Data Mining Techniques is called Web Mining. Web Mining is broadly classified in to three types namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this paper our focus is mainly on Web Usage Mining, where we are applying the data mining techniques to analyse and discover interesting knowledge from the Web Usage data. The activities of the user are captured and stored at different levels such as server level, proxy level and user level called as Web Usage Data and the usage data stored at server side is Web Server Log, where it records the browsing behavior of users and their requests based on the user clicks. Web server Log is a primary source to perform Web Usage Mining. This paper also brings in to discussion of various existing pre-processing techniques and analysis of web log files and how clustering is applied to group the users based on the browsing behavior of users on their interested contents.

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