Rare itemsets are likely to be of great interest because they often relate to high-impact transactions which may give rise to rules of great practical significance. Research into the rare association rule mining problem has gained momentum in the recent past. In this paper, we propose a novel approach that captures such rare rules while ensuring that redundant rules are eliminated. Extensive te...

Mining frequently occurring patterns or itemsets is a fundamental task in datamining. Many ad-hoc itemset mining algorithms have been proposed for enumerating frequent, maximal and closed itemsets. The datamining community has been particularly interested in finding itemsets that satisfy additional constraints, which is a challenging task for existing techniques. In this paper we present a nove...

38 Computer E stablished companies have had decades to accumulate masses of data about their customers , suppliers, and products and services. The rapid pace of e-commerce means that Web startups can become huge enterprises in months, not years, amassing proportionately large databases as they grow. Data mining, also known as knowledge discovery in databases, 1 gives organizations the tools to ...

Frequent Itemset Mining (FIM) is one of the most eminent techniques in the Data mining systems. The exploration of Frequent Itemset Mining distills the recurring knowledge from the incessant data. Explosion of Frequent Itemset Mining in the field of Data Analysis and Data Mining becomes an inescapable one. The paper focuses on “searching the accurate records of efficient database queries withou...

—Frequent itemset mining has emerged as a fundamental problem in data mining and plays an important role in many data mining tasks, such as association analysis, classification, etc. In the framework of frequent itemset mining, the results are itemsets that are frequent in the whole database. However, in some applications, such recommendation systems and social networks, people are more interes...

Data items have been extracted using an empirical data mining technique called frequent itemset mining. In majority of theapplication contexts items are enriched with weights. Pushing an item weights into the itemset extraction process, i.e., mining weighted itemsets rather than traditional itemsets, is an appealing research direction. Although many efficient weighteditemset mining algorithms a...

Proposing efficient techniques for discovery of useful information and valuable knowledge from very large databases and data warehouses has attracted the attention of many researchers in the field of data mining. The wellknown Association Rule Mining (ARM) algorithm, Apriori, searches for frequent itemsets (i.e., set of items with an acceptable support) by scanning the whole database repeatedly...

The vast amount of textual information available in electronic form is growing at a staggering rate in recent times. The task of mining useful or interesting frequent itemsets (words/terms) from very large text databases that are formed as a result of the increasing number of textual data still seems to be a quite challenging task. A great deal of attention in research community has been receiv...