We consider the problem of mining high-utility plans from historical plan databases that can be used to transform customers from one class to other, more desirable classes. Traditional data mining algorithms are focused on finding frequent sequences. But high frequency may not imply low costs and high benefits. Traditional Markov Decision Process (MDP) algorithms are designed to address this is...

High-Utility Itemset Mining (HUIM) is an extension of frequent itemset mining, which discovers itemsets yielding a high profit in transaction databases (HUIs). In recent years, a major issue that has arisen is that data publicly published or shared by organizations may lead to privacy threats since sensitive or confidential informationmay be uncovered by data mining techniques. To address this ...

The rationale behind mining frequent itemsets is that only itemsets with high frequency are of interest to users. However, the practical usefulness of frequent itemsets is limited by the significance of the discovered itemsets. A frequent itemset only reflects the statistical correlation between items, and it does not reflect the semantic significance of the items. In this paper, we propose a u...

There is a tremendous increase in the research of data mining. Data mining is the process of extraction of data from large database. Knowledge Discovery in database (KDD) is another name of data mining. Privacy protection has become a necessary requirement in many data mining applications due to emerging privacy legislation and regulations. One of the most important topics in research community...

Utility mining finds out high utility itemsets by considering both the profits and quantities of items in transactions. In this paper, a three-scan mining approach is proposed to efficiently discover high utility itemsets from transaction databases. The proposed approach utilizes an itemset-generation mechanism to prune redundant candidates early and to systematically check the itemsets from tr...

It is a well accepted verity that the process of data mining produces numerous patterns from the given data. The most significant tasks in data mining are the process of discovering frequent itemsets and association rules. Numerous efficient algorithms are available in the literature for mining frequent itemsets and association rules. Incorporating utility considerations in data mining tasks is...

. Mining frequent log items is an active area in data mining that aims at searching interesting relationships between items in databases. It can be used to address a wide variety of problems such as discovering association rules, sequential patterns, correlations and much more. Weblog that analyzes a Web site's access log and reports the number of visitors, views, hits, most frequently visited ...

This article proposes an innovative utility sentient approach for the mining of interesting association patterns from transaction databases. First, frequent patterns are discovered from the transaction database using the FPGrowth algorithm. From the frequent patterns mined, this approach extracts novel interesting association patterns with emphasis on significance, utility, and the subjective i...

High utility pattern (HUP) mining over data streams has become a challenging research issue in data mining. The existing sliding window-based HUP mining algorithms over stream data suffer from the level-wise candidate generationand-test problem. Therefore, they need a large amount of execution time and memory. Moreover, their data structures are not suitable for interactive mining. To solve the...