PERFORMANCE OF K-MEANS CLUSTERING AND BIRD FLOCKING ALGORITHM FOR GROUPING THE WEB LOG FILES

摘要

Data mining is the process of analyzing the interesting pattern and knowledge in different perspectives and summarizing it into useful information from the large amount of data. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. The unlabled vast amount of data can be grouped using clustering or classification algorithms. Cluster analysis or clustering is the task of assigning a set of objects into groups called clusters. So, the objects in the same cluster are more similar to each other than to those in other clusters. Many of the researchers evaluated the performance of the familiar K-means clustering algorithm and attempt to improve the efficiency of the algorithm. This paper will analyze the performance of the K-means clustering algorithm with the biological based algorithm called Bird flocking algorithm for grouping the web logs. Web logs are unformatted text files which contains the information regarding the users browser detail. The proposed system takes the input as web log files and groups the web sites based on the interesting rate of the users. The performance is evaluated in terms of no of clusters, CPU utilization time and accuracy.