Clustering Sequential data with OPTICS
The Web has enormous, various and knowledgeable data for data mining research. The web is a biggest knowledgeable database with various types of data to mine. One of interesting type of data is user behaviour that mine from server log files. Many algorithms are for clustering and then discover the knowledge from database. In this paper we use OPTICS (Ordering Points To Identify the Clustering Structure) algorithm to find density based clusters on a social music website data (Last.fm website is a free social platform that share listed music with so different music genres). After pre-processing on music dataset and removing unprofitable data from the dataset was ready to clustering. The clusters are generated by OPTICS algorithm and the average of inter cluster and intra cluster are calculated. Then results are visualized and Euclidean distance measure is used to compare results of intra cluster and inter cluster analyses. Finally showed behavior of clusters that made by OPTICS algorithm on a sequential data.
Clustering algorithm OPTICS Sequence mining
Amin Omrani K.Santhisree Damodaram
Dept. of Computer science, Jawaharlal Nehru Technology University (JNTUH), Hyderabad, India
国际会议
西安
英文
591-594
2011-05-13（万方平台首次上网日期，不代表论文的发表时间）