MINING CUSTOMER BEHAVIOUR USING WEB USAGE MINING IN E-COMMERCE

GALGOTIAS COLLEGE OF ENGG. AND TECH.

Department of Computer Science & EngineeringDOMAIN : WEB MINING

MINING CUSTOMER BEHAVIOUR USING WEB USAGE MINING IN E-COMMERCEPROJECT GUIDE:Mr. R.P. SINGH

SUBMITTED BY:RAHUL PANT1109710081SHOBHIT GARG 1109710101SUMIT CHAUDHARY1109710109

CONTENTSABSTRACTINTRODUCTIONCLASSIFICATION OF WEB MININGPROCESS OF WEB MININGWEB MINING IN E-COMMERCERELATED WORKPATTERN RECOGNITION SYSTEMPRO[OSED WORKK-MEAN CLUSTERING ALGORITHMDEMONSTRATION OF ALGORITHMREFRENCES

ABSTRACTThe main purpose of this project is to study the customer's behavior using the Web mining techniques and its application in e-commerce to mine customer behavior. The concept of Web mining describing the process of Web data mining in detail: source data collection, data preprocessing, pattern discovery, pattern analysis and cluster analysis. The principle of data mining is to cluster customer segments by using K-Means algorithm in which input data comes from web log of various e-commerce websites. Hence, determine the relationship between Web data mining and ecommerce and also to apply Web mining technology in ecommerce.

INTRODUCTIONData mining is to extract information and knowledge which is not known by people and potentially useful from a large number of incomplete and vague random data of practical application.

Web mining is the application of data mining technology, which is to extract interesting and potentially useful patterns and hidden information from web documents and web activities.

CLASSIFICATION OF WEB MININGWeb Mining is broadly categorized into :WEB CONTENT MINING (WCM).WEB STRUCTURE MINING (WSM).WEB USAGE MINING (WUM).

PROCESS OF WEB MINING

The process of Web mining is divided into four stages:

SOURCE DATA COLLECTIONDATA PREPROCESSINGPATTERN DISCOVERIESPATTERN ANALYSIS

WEB MINING IN E-COMMERCEA most important challenge of E-commerce is to understand customer's wants, love and value orientation as much as possible, in order to make sure competitiveness in e-commerce era. Web mining can be used to find obvious data which have potential value.

PERSONALISED SERVICEIMPROVE E-COMMERCE WEBSITE DESIGNADVERTISING EFFECTIVENESS EVALUATION

It reflects five hierarchical structure included in the recognition system.APPLICATION LAYERSYSTEM CONTROL LAYERDATA ANALYSIS LAYERINFORMATION RESOURCE LAYERBASE MANAGEMENT LAYERRELATED WORK

PATTERN RECOGNITION SYSTEM

PROPOSED WORKOn the basis of Web log mining we create clusters of the customer's behavior.In Web log there are two kinds of clusters: client cluster and page cluster.Similar visiting behavior of client is clustered in client cluster.In Page cluster the pages with related contents are gathered that are of same kind.Here we have created the cluster using K-means algorithm and tool used is Mine set for data analysis.

K-MEANS Clustering Algorithmk-means clusteringis a method ofvector quantization, originally from signal processing, that is popular forcluster analysisindata mining.k-means clustering aims topartitionnobservations intokclusters in which each observation belongs to the cluster with the nearestmean, serving as aprototypeof the cluster.

STANDARD ALGORITHMThe most common algorithm uses an iterative refinement technique. Due to its ubiquity it is often called thek-means algorithmGiven an initial set ofkmeansm1(1),,mk(1)(see below), the algorithm proceeds by alternating between two steps :

Demonstration of the algorithm

DRAWBACK OF K-MEANS ALGORITHMIt does not yield the same result with each run since the resulting clusters depend on the initial random assignments.Difficult to predict k-valueWith global cluster, it didn't work well.

CONCLUSIONAn effective method is to be proposed to compare variable length sessions and basic k-means algorithm is to be modified to get effective clusters such that the initial centroid assignments will not have much impact on the final clusters.

REFERENCESResearch Paper : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6395938&queryText%3Dweb+mining+e+commercehttp://en.wikipedia.org/wiki/Web_mininghttp://en.wikipedia.org/wiki/Cluster_analysishttp://en.wikipedia.org/wiki/Data_mininghttp://en.wikipedia.org/wiki/K-means_clustering