Mining Frequent Item and Item Sets Using Fuzzy Slices

Abstract

Data mining is an increasingly important technology for extracting useful knowledge hidden in large collections of data. The proposed work presents the design of mining frequent items from dataset. The mining approach is hybrid, that is, frequent items are calculated with a single pass, while frequent item sets are calculated by a further multi-pass analysis. Frequent items mining is to process a stream of items and find all items occurring more than a given fraction of the time. In particular, data stream analysis has been carried out for the computation of items and item sets that exceed a frequency threshold. In the proposed work Mining of frequent item and item sets is based on fuzzy slices. Fuzzy approaches can play an important role in data mining, because they provide comprehensible results. In addition, the approaches studied in data mining have mainly been oriented at highly structured and precise data. However, in mining the analysis of more complex heterogeneous information will become more important in the near future. Keywords-Data Mining, Frequent Item, Frequent Item sets, Fuzzy slices.