• Journal of the Korean Data and Information Science Society
• /
• v.25 no.1
• /
• pp.97-106
• /
• 2014

The development of big data technology was to more accurately predict diversified contemporary society and to more efficiently operate it, and to enable impossible technique in the past. This technology can be utilized in various fields such as the social science, economics, politics, cultural sector, and science technology at the national level. It is a prerequisite to find valuable information by data mining techniques in order to analyze big data. Data mining techniques associated with big data involve text mining, opinion mining, cluster analysis, association rule mining, and so on. The most widely used data mining technique is to explore association rules. This technique has been used to find the relationship between each set of items based on the association thresholds such as support, confidence, lift, similarity measures, etc.This paper proposed cosine net confidence as association thresholds, and checked the conditions of interestingness measure proposed by Piatetsky-Shapiro, and examined various characteristics. The comparative studies with basic confidence and cosine similarity, and cosine net confidence were shown by numerical example. The results showed that cosine net confidence are better than basic confidence and cosine similarity because of the relevant direction.