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KNOWLEDGE-DISCOVERY IN DATABASES

Platform : JAVA

Non IEEE Project : 2011

ABSTRACT Grouping customer transactions into segments may help understand customers better. The marketing literature has concentrated on identifying important segmentation variables (e.g., customer loyalty) and on using cluster analysis and mixture models for segmentation. The data mining literature has provided various clustering algorithms for segmentation without focusing specifically on clustering customer transactions. Building on the notion that observable customer transactions are generated by latent behavioral traits, in this project, we investigate using a pattern-based clustering approach to grouping customer transactions. We define an objective function that we maximize in order to achieve a good clustering of customer transactions and present an algorithm, GHIC, that groups customer transactions such that item sets generated from each cluster, while similar to each other, are different from ones generated from others. We present experimental results from user-centric Web usage data that demonstrates that GHIC generates a highly effective clustering of transactions Existing System The Data mining Algorithms can be categorized into the following :  Association Algorithm  Classification  Clustering Algorithm Classification: The process of dividing a dataset into mutually exclusive groups such that the members of each group are as "close" as possible to one another, and different groups are as "far" as possible from one another, where distance is measured with respect to specific variable(s) you are trying to predict. For example, a typical classification problem is to divide a database of companies into groups that are as homogeneous as possible with respect to a creditworthiness variable with values "Good" and "Bad." Clustering: The process of dividing a dataset into mutually exclusive groups such that the members of each group are as "close" as possible to one another, and different groups are as "far" as possible from one another, where distance is measured with respect to all available variables. Proposed System With the explosive growth of information sources available on the World Wide Web, it has become increasingly necessary for users to utilize automated tools in find the desired information resources, and to track and analyze their usage patterns. Web Mining is the extraction of interesting and potentially useful patterns and implicit information from artifacts or activity related to the Worldwide Web. Web servers record and accumulate data about user interactions whenever requests for resources are received. Analyzing the web access logs of different web sites can help understand the user behavior and the web structure, thereby improving the design of this colossal collection of resources. The data mining algorithm well suited for Web Mining is Clustering Algorithm, So We have taken Web Browsing data related to Web and Clustering Technique is used for that data to know about the features like time (e.g., average time spent per page), quantity (e.g., number of sites visited), and order of pages visited (e.g., first site) and therefore include both categorical and numeric types. These techniques can be used in the area like • E-commerce • Search Engines • Personalization • Website Design System requirements: Software requirements: Operating System : Windows XP Technology : Java 1.4 Web Technologies : Html, JavaScript Web Server : Tomcat 5.5 Database : Oracle 10g Hardware requirements: Processor : Pentium IV processor Hard Disk : 20GB RAM : 128MB

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