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 ...

Data mining is the process of mining new non trivial and potentially valuable information from large data basis. Data mining has been used in the analysis of customer transaction in retail research where it is termed as market basket analysis. Earlier data mining methods concentrated more on the correlation between the items that occurs more frequent in the transaction. In frequent itemset mini...

Data mining can be used extensively in the enterprise based applications with business intelligence characteristics to provide a deeper kind of analysis while meeting strict requirements for administration management and security. Business intelligence is information about a company's past performance that is used to help predict the company's future performance. ARM is a well-known technique i...

Abstract Recently, revealing more valuable information except for quantity value a database is an essential research field. High utility itemset mining (HAUIM) was suggested to reveal useful patterns by average-utility measure pattern analytics and evaluations. HAUIM provides fair assessment than generic high ignores the influence of length itemsets. There are several high-performance algorithm...

High utility itemset mining is an interesting research in the field of data mining, which can find more valuable information than frequent mining. Several high-utility approaches have already been proposed; however, they high computational costs and low efficiency. To solve this problem, a algorithm based on particle filter proposed. This approach first initializes population, consists sets. Th...

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...

High-utility itemset mining (HUIM) is an important data mining task with wide applications. In this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discovers high-utility itemsets both in terms of execution time and memory. EFIM relies on two upper-bounds named sub-tree utility and local utility to mo...

The main goals of Association Rule Mining (ARM) are to find all frequent itemsets and to build rules based of frequent itemsets. But a frequent itemset only reproduces the statistical correlation between items, and it does not reflect the semantic importance of the items. To overcome this limitation we go for a utility based itemset mining approach. Utility-based data mining is a broad topic th...

Utility itemsets typically consist of items with different values such as utilities, and the aim of utility mining is to identify the itemsets with highest utilities. In the past studies on utility mining, the values of utility itemsets were considered as positive. In some applications, however, an itemset may be associated with negative item values. Hence, discovery of high utility itemsets wi...