Data mining, Web data Mining, Association mining, FP-Growth
The aim of data mining is to find the hidden meaningful knowledge from huge amount of data stored on web. One of the important areas of data mining is web mining. Web Data Mining is an very important area of Data Mining which deals with the extraction of interesting and hidden knowledge from the World Wide Web, It can be classified into three different types i.e. web structure mining, web content mining and web usages mining. However, in web data mining, patterns are extracted from databases. Web data mining refers to a collection of methods used to find patterns and intelligence from the data. The patterns that can be discovered may be descriptive that describe the general properties of the existing data, and predictive which attempt to do predictions based on inference on available data. There are many technique used for finding frequent pattern.
Along with the present existing techniques, the frequent pattern growth (FP-growth) technique is the most effective and scalable approach. We tend to propose an improved technique that extracting association rules from web data knowledge without any preprocessing or post processing. We propose improved algorithm, for mining the entire set of frequent patterns by pattern fragment growth. First Frequent Pattern-tree based mining adopts a pattern fragment growth technique to avoid the costly generation of a large number of candidate sets and a partition-based, divide-and-conquer technique is used.