Utility mining is the study of itemset mining from the consideration of utilities. It is the utility-based itemset mining approach to find itemsets conforming to user preferences. Modern research in mining high-utility itemsets (HUI) from the databases faces two major challenges: exponential search space and database-dependent minimum utility threshold. The search space is extremely vast when t...

Mining user behaviors in mobile environments is an emerging and important topic in data mining fields. Previous researches have combined moving paths and purchase transactions to find mobile sequential patterns. However, these patterns cannot reflect actual profits of items in transaction databases. In this work, we explore a new problem of mining high utility mobile sequential patterns by inte...

Utility of an itemset is considered as the value of this itemset, and utility mining aims at identifying the itemsets with high utilities. The temporal high utility itemsets are the itemsets whose support is larger than a pre-specified threshold in current time window of the data stream. Discovery of temporal high utility itemsets is an important process for mining interesting patterns like ass...

data sanitization is a process that is used to promote the sharing of transactional databases among organizations and businesses, it alleviates concerns for individuals and organizations regarding the disclosure of sensitive patterns. it transforms the source database into a released database so that counterparts cannot discover the sensitive patterns and so data confidentiality is preserved ag...

The itemsets discovered by traditional High Utility Itemsets Mining (HUIM) methods are more useful than frequent itemset mining outcomes; however, they are usually disordered and not actionable, and sometime accidental, because the utility is the only judgement and no relations among itemsets are considered. In this paper, we introduce the concept of combined mining to select combined itemsets ...

Utility mining has recently been an emerging topic in the field of data mining. It finds out high-utility itemsets by considering both the important factors of profit and quantity. In some situations, rarely occurring items may co-occur in a relatively close relationship with specific high-utility items. These utility itemsets with rare items may provide useful information to decision makers as...

Fuzzy utility mining reflects fuzzy degrees of quantities and profits for high utility itemsets. In generally, transaction time is also concerned, and not all products sold are always on the shelf. Thus, in this paper we present an effective framework, which considers the transaction period of each product from the first transaction it appears to the last transaction in the whole database for m...

A pattern is of utility to a person if its use by that person contributes to reaching a goal. Utility based measures use the utilities of the patterns to reflect the user’s goals. In this paper, we first review utility based measures for itemset mining. Then, we present a unified framework for incorporating several utility based measures into the data mining process by defining a unified utilit...

High utility itemsets mining extends frequent pattern mining to discover itemsets in a transaction database with utility values above a given threshold. However, mining high utility itemsets presents a greater challenge than frequent itemset mining, since high utility itemsets lack the anti-monotone property of frequent itemsets. Transaction Weighted Utility (TWU) proposed recently by researche...

Association Rule Mining (ARM) is one of the most popular data mining techniques. Most of the past work is based on frequent item-set. In current years, the concentration of researchers has been focused on infrequent item-set mining. The infrequent item-set mining problem is discovering item-sets whose frequency of the data is less than or equal to maximum threshold. This paper addresses the min...