A distributed algorithm based on Dynamic Itemset Counting (DIC) for generation of frequent itemsets is presented by us. DIC represents a paradigm shift from Apriori-based algorithms in the number of passes of the database hence reducing the total time taken to obtain the frequent itemsets. We exploit the advantage of Dynamic Itemset Counting in our algorithmthat of starting the counting of an i...

Over the last decades, frequent itemset mining has become a major area of research, with applications including indexing and similarity search, as well as mining of data streams, web, and software bugs. Although several efficient techniques for generating frequent itemsets with a minimum support (frequency) have been proposed, the number of itemsets produced is in many cases too large for effec...

Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally...

The purpose of this paper is two-fold: First, we give efficient algorithms for answering itemset support queries for collections of itemsets from various representations of the frequency information. As index structures we use itemset tries of transaction databases, frequent itemsets and their condensed representations. Second, we evaluate the usefulness of condensed representations of frequent...

Since its introduction, frequent itemset mining has been the subject of numerous studies. However, most of them return frequent itemsets in the form of textual lists. The common cliché that “a picture is worth a thousand words” advocates that visual representation can enhance user understanding of the inherent relations in a collection of objects such as frequent itemsets. Many visualization sy...

The Apriori algorithm’s frequent itemset approach has become the standard approach to discovering association rules. However, the computation requirements of the frequent itemset approach are infeasible for dense data and the approach is unable to discover infrequent associations. OPUS AR is an efficient algorithm for association rule discovery that does not utilize frequent itemsets and hence ...

www.ijitam.org Abstract These Apriori Algorithm is one of the wellknown and most widely used algorithm in the field of data mining. Apriori algorithm is association rule mining algorithm which is used to find frequent itemsets from the transactions in the database. The association rules are then generated from these frequent itemsets. The frequent itemset mining algorithms discover the frequent...

Frequent itemset mining is one of fundamental data mining problems that shares many similarities with traditional database querying. Hence, several query optimization techniques known from database systems have been successfully applied to frequent itemset queries, including reusing results of previous queries and multi-query optimization. In this paper, we consider a new problem of processing ...

The mining of frequent itemsets is often challenged by the length of the patterns mined and also by the number of transactions considered for the mining process. Another acute challenge that concerns the performance of any association rule mining algorithm is the presence of „null‟ transactions. This work proposes a closed frequent itemset mining algorithm viz., Closed Frequent Itemset Mining a...

We consider the problem of optimizing processing of batches of frequent itemset queries. The problem is a particular case of multiple-query optimization, where the goal is to minimize the total execution time of the set of queries. We propose an algorithm that is a combination of the Mine Merge method, previously proposed for processing of batches of frequent itemset queries, and the Partition ...