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Date Submitted: 07/09/2011 10:33 PM
2008 Workshop on Knowledge Discovery and Data Mining
Mining the E-commerce Data to Analyze the Target Customer Behavior
Yuantao Jiang jiangytao@yahoo.com.cn Abstract
In the advent of the information era, e-commerce has developed rapidly and has become significant for every business. With the advanced information technologies, firms are now able to collect and store mountains of data describing their myriad offerings and diverse customer profiles, from which they seek to derive information about their customers’ needs and wants. Traditional forecasting methods are no longer suitable for these business situations. This research used the principles of data mining to cluster customer segments by using K-Means algorithm and data from web log of various e-commerce websites. Consequently, the results showed that there was a clear distinction between the segments in terms of customer behavior.
Siqin Yu ysq@shmtu.edu.cn
of non-obvious yet useful information for decision makers from very large databases. The various mechanisms of this generation include abstractions, aggregations, summarizations, and characterizations of data[1]. These forms, in turn, are the result of applying sophisticated modeling techniques from the diverse fields of statistics, artificial intelligence, database management and computer graphics. Collecting consumer information seemed have been available, but still how to analyze these data effectively is of interest to marketers and researchers. The traditional methods for predicting and analyzing customer demands have found a wide range of applications. They are mainly used for predicting the total quantity of products that belong to the same family rather than the relationship between the different customer groups and associated product groups. This paper clusters customer segments by using K-Means algorithm and data from web log of various e-commerce websites. Consequently, the results showed that there was a clear distinction between the...