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: High utility itemset mining has emerged to be an important research issue in data mining since it has a wide range of real life applications. Although a number of algorithms have been proposed in recent years, there seems to be still a lack of efficient algorithms since these algorithms suffer from either the problem of low efficiency of calculating candidates’ utilities or the proble...

High-utility itemset mining is the task of discovering highutility itemsets, i.e. sets of items that yield a high profit in a customer transaction database. High-utility itemsets are useful, as they provide information about profitable sets of items bought by customers to retail store managers, which can then use this information to take strategic marketing decisions. An inherent limitation of ...

Data Mining is the process of analyzing data from different perspectives and summarizing it into useful information. It can be defined as the activity that extracts information contained in very large database. That information can be used to increase the revenue or cut costs. Association Rule Mining (ARM) is finding out the frequent itemsets or patterns among the existing items from the given ...

Due to the increasing use of very large databases and data warehouses, mining useful information and helpful knowledge from transactions is evolving into an important research area. In the past, researchers usually assumed databases were static and items were on a single level to simplify data mining problems. Thus, most of algorithms proposed focused on a single level, and did not utilize prev...

Discovery of frequent itemsets is a very important data mining problem with numerous applications. Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. A significant amount of research on frequent itemset mining has been done so far, focusing mainly on developing faster complete mining al...

Frequent generalized itemset mining is a data mining technique utilized to discover a high-level view of interesting knowledge hidden in the analyzed data. By exploiting a taxonomy, patterns are usually extracted at any level of abstraction. However, some misleading high-level patterns could be included in the mined set. This paper proposes a novel generalized itemset type, namely theMisleading...

Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of...

--The utility of an itemset represents its importance, which can be measured in terms of weight, value, quantity or other information depending on the user specification. High utility itemsets mining identifies itemsets whose utility satisfies a given threshold. It allows users to quantify the usefulness or preferences of items using different values. Thus, it reflects the impact of different i...

High-utility itemset mining (HUIM) is an important research topic in data mining field and extensive algorithms have been proposed. However, existing methods for HUIM present too many high-utility itemsets (HUIs), which reduces not only efficiency but also effectiveness of mining since users have to sift through a large number of HUIs to find useful ones. Recently a new representation, closed +...