In this paper, we introduce a new kind of patterns, named high on-shelf utility itemsets, which consider not only individual profit and quantity of each item in a transaction but also on-shelf periods in a database. We have thus proposed a 3-scan mining algorithm to efficiently discover the itemsets. The proposed approach adopts a new pruning strategy and an itemset-generation mechanism to prun...

Let X be a connected metric space, and let ⪰ weak order defined on suitable subset of N . We characterize when has Cesàro average utility representation This means that there is continuous real-valued function u such that, for all sequences x = ( n ) 1 ∞ y in the domain ⪰, we have if only limit as → value , … higher than applications to decision theory, game intergenerational social choice.

In this shrinking modern business world making right decisions at right time is the key to success. Helping the business community to make right decision and choices, the KDD evolved itself from association rule mining and frequent patterns to the recent more complex task of mining High Utility Itemset. Many approaches have been proposed to mine HUI in recent years. But almost many of the metho...

Data mining is an essential part of knowledge discovery, and performs the extraction of useful information from a collection of data, so as to assist human beings in making necessary decisions. This thesis describes research in the field of itemset mining, which performs the extraction of a set of items that occur together in a dataset, based on a user specified threshold. Recent focus of items...

Data mining plays a vital role in today’s information world wherein it has been widely applied in various business organizations. The current trend in business collaboration demands the need to share data or mined results to gain mutual benefit. However it has also raised a potential threat of revealing sensitive information when releasing data. Data sanitization is the process to conceal the s...

The paradigm shift from ̳data-centered pattern mining‘ to ̳domain driven actionable knowledge discovery‘ has increased the need for considering the business yield (utility) and demand or rate of recurrence of the items (frequency) while mining a retail business transaction database. Such a data mining process will help in mining different types of itemsets of varying business utility and demand....

-Association rule has been an area of active research in the field of knowledge discovery. Data mining researchers had improved upon the quality of association rule mining for business development by incorporating influential factors like value (utility), quantity of items sold (weight) and more for the mining of association patterns. In this paper, we propose an efficient approach to find maxi...

Generating a huge number of association rules reduces their utility in the decision making process, done by domain experts. In this context, based on the theory of Formal Concept Analysis, we propose to extend the notion of Formal Concept through the generalization of the notion of itemset in order to consider the itemset as an intent, its support as the cardinality of the extent and its releva...

Data mining is the practice to search large amount of data to discover data patterns. Data mining uses mathematical algorithms to group the data and evaluate the future events. Association rule is a research area in the field of knowledge discovery. Many data mining researchers had improved upon the quality of association rule for business development by incorporating influential factors like u...