Association rule mining has been an area of active research in the field of knowledge discovery and numerous algorithms have been developed to this end. Of late, data mining researchers have improved upon the quality of association rule mining for business development by incorporating the influential factors like value (utility), quantity of items sold (weight) and more, for the mining of assoc...

High-utility sequential pattern mining is an emerging topic in recent decades and most algorithms were designed to identify the complete set of high-utility sequential patterns under the single minimum utility threshold. In this paper, we first propose a novel framework called high-utility sequential pattern mining with multiple minimum utility thresholds to mine high utility sequential pattern...

-Mining frequent itemsets is an active area in data mining that aims at searching interesting relationships between items in databases. It can be used to address to a wide variety of problems such as discovering association rules, sequential patterns, correlations and much more. Existing methods often generate a huge set of potential high utility item sets and their mining performance is degrad...

One of the important research area in data mining is high utility pattern mining. Discovering itemsets with high utility like profit from database is known as high utility itemset mining. There are number of existing algorithms have been work on this issue. Some of them incurs problem of generating large number of candidate itemsets. This leads to degrade the performance of mining in case of ex...

Most approaches to mining association rules implicitly consider the utilities of the itemsets to be equal. We assume that the utilities of itemsets may differ, and identify the high utility itemsets based on information in the transaction database and external information about utilities. Our theoretical analysis of the resulting problem lays the foundation for future utility mining algorithms.

Association-rule mining is commonly used to discover useful and meaningful patterns from a very large database. It only considers the occurrence frequencies of items to reveal the relationships among itemsets. Traditional association-rule mining is, however, not suitable in real-world applications since the purchased items from a customer may have various factors, such as profit or quantity. Hi...

This paper focuses on the various techniques that can be implemented for classification of observations that are initially uncategorized. Our objective is to compare the different classification methods and classifiers that can be used for this purpose. In this paper, we study and demonstrate the different accuracies and usefulness of classifiers and the circumstances in which they should be im...

Many measures have been proposed and studied extensively in data mining for evaluating the usefulness or interestingness of discovered rules. They are normally defined based on structural characteristics or statistical information about the rules. The meaningfulness of each measure is interpreted based on some intuitive argument or mathematical properties. There does not exist a framework in wh...

Efficient discovery of frequent itemsets in large datasets is a crucial task of data mining. In recent years, several approaches have been proposed for generating high utility patterns, they arise the problems of producing a large number of candidate itemsets for high utility itemsets and probably degrades mining performance in terms of speed and space. Recently proposed compact tree structure,...

Privacy preserving data mining (PPDM) has become a popular research direction in data mining. Privacy preserving data mining is an approach to develop algorithms by which we can modify the utility values of original data using some techniques in order to protect sensitive information from unauthorized user. Protecting data against illegal access becomes a serious issue when this data is require...