A Fuzzy Approach to Weighted Temporal Mining Using Automatic Weight Assignment with Reduced Complexities

Abstract

Databases and data warehouses have become a vital part of many organizations. So useful information and helpful knowledge have to be mined from transactions. In real life, media information has time attributes either implicitly or explicitly called as temporal data. The temporal database consists of items which are prioritized by assigning weights. These weights are assigned automatically using the scheduling concept aging algorithm in such a way that starvation is avoided. This paper focuses on an encoding method for the temporal database that reduces the memory utilization during processing. The fuzzy approach is applied by assigning the triangular membership functions to the weighted items which gives better results than quantitative values. Weighted association rule mining is performed using the weighted support and confidence measures which are applied to the AprioriTid algorithm as a new approach to the already existing algorithm. This makes the modified algorithm compatible to mine weighted association rules. This approach gives better results than that which treats items uniformly, leading to weighted association rule mining on an encoded temporal database with reduced time and computational complexity. The experimental results are drawn from the complaints database of the telecommunication system. This database technology can be used to enhance the complaint addressing system through the web which is an upgraded technique for the semantic web.