An Empirical Study for Mining Association Rules on Individal Cluster

• Main Author: 施威宏
• Other Authors: 施明毅
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/09743957584415641657

碩士 === 國立彰化師範大學 === 資訊工程學系 === 97 === Clustering and association rules mining are two important methods in data mining. Clustering is used to divide the similar data into the same group. By analyzing these formed groups, users can easily abtain the hidden pattern from data. However when datasets are large and complicated, analyzing characteristic of each group will become difficult. Association rules mining can find out frequent itemsets that items always appear in the same transaction. The results of mined rules will show the relationships among items. Thus, there are some weaknesses for association rules mining. When datasets are large, it maybe produces a large amount of rules. The other weakness is that items with few appearance in transactions will be eliminated during finding frequent items process; therefore information about these items will be unavaiable. In this paper, we apply clustering and association rules mining on data collected from http://ww... to analyaze users’ behaviors. First, using clustering algorithm divide the dataset into several groups. Then using association rules mining find out important rules from each groups. The experimental evidence shows that combining these two approachs can improve the disvantages of adopting clustering or association rules mining alone. Therefore it will help users to analyze data easily and efficiently.