A complete set of frequent itemsets can get undesirably large due to redundancy. Several representations have been proposed to eliminate the redundancy. Existing generator based representations rely on a negative border to make the representation lossless. However, negative borders of generators are often very large. The number of itemsets on a negative border sometimes even exceeds the total n...

The discovery of frequent itemsets can serve valuable economic and research purposes. Releasing discovered frequent itemsets, however, presents privacy challenges. In this paper, we study the problem of how to perform frequent itemset mining on transaction databases while satisfying differential privacy. We propose an approach, called PrivBasis, which leverages a novel notion called basis sets....

Recent advances in computer technology in terms of speed, cost, tremendous amount of computing power and decrease data processing time has spurred increased interest in data mining applications to extract useful knowledge from data. Over the last couple of years, data mining technology has been successfully employed to various business domains and scientific areas. Various data mining technique...

Frequent itemset originates from association rule mining. Recently, it has been applied in text mining such as document categorization, clustering, etc. In this paper, we conduct a study on text clustering using frequent itemsets. The main contribution of this paper is three manifolds. First, we present a review on existing methods of document clustering using frequent patterns. Second, a new m...

In recent times, the vast amount of textual information available in electronic form is growing at staggering rate. This increasing number of textual data has led to the task of mining useful or interesting frequent itemsets (words/terms) from very large text databases and still it seems to be quite challenging. The use of such frequent itemsets for text clustering has received a great deal of ...

Mining frequent patterns in transaction databases and many other kinds of databases has been studied popularly in data mining research. Methods for efficient mining of frequent itemsets have been studied extensively by many researchers. However, the previously proposed methods still encounter some performance bottlenecks when mining databases with different data characteristics. The time requir...

The purpose of this work is to mine closed frequent itemsets from transactional data streams using a sliding window model. An efficient algorithm IMCFI is proposed for Incremental Mining of Closed Frequent Itemsets from a transactional data stream. The proposed algorithm IMCFI uses a data structure called INdexed Tree(INT) similar to NewCET used in NewMoment[5]. INT contains an index table Item...

In this paper, we study an inherent problem of mining Frequent Itemsets (FIs): the number of FIs mined is often too large. The large number of FIs not only affects the mining performance, but also severely thwarts the application of FI mining. In the literature, Closed FIs (CFIs) and Maximal FIs (MFIs) are proposed as concise representations of FIs. However, the number of CFIs is still too larg...