抄録
Data mining is the process to find some patterns or rules from the large volumes of data automatically. It is classified into six fields ; classification, estimation, prediction, association rule, clustering and profiling. We focus on data clustering and classify the similar data into same clusters. These clusters can be used in preparation of data analysis, finding segmentations in market and so on. We apply "Multiobjective clustering with automatic determination of the number of clusters: MOCK". MOCK uses two complementary objectives based on cluster compactness and connectedness, and returns a set of different trade-off partitioning over a range of different cluster numbers, k. It is able to find the appropriate number of clusters based on the information of the trade-off curve. In this paper, we considered the scalability of MOCK with along to the increase of the number of the data. Especially, we used web data clustering for examining the scalability.
