Application of Utility Mining using Frequent Itemset and Association Rules: A Survey (original) (raw)

Optimized High-Utility Itemsets Mining for Effective Association Mining Paper

International Journal of Electrical and Computer Engineering (IJECE), 2017

Association rule mining is intently used for determining the frequent itemsets of transactional database; however, it is needed to consider the utility of itemsets in market behavioral applications. Apriori or FP-growth methods generate the association rules without utility factor of items. High-utility itemset mining (HUIM) is a well-known method that effectively determines the itemsets based on high-utility value and the resulting itemsets are known as high-utility itemsets. Fastest high-utility mining method (FHM) is an enhanced version of HUIM. FHM reduces the number of join operations during itemsets generation, so it is faster than HUIM. For large datasets, both methods are very expenisve. Proposed method addressed this issue by building pruning based utility co-occurrence structure (PEUCS) for elimatination of low-profit itemsets, thus, obviously it process only optimal number of high-utility itemsets, so it is called as optimal FHM (OFHM). Experimental results show that OFHM takes less computational runtime, therefore it is more efficient when compared to other existing methods for benchmarked large datasets. 1. INTRODUCTION Association rule mining methods [1] are used for discovering rules and items that are of frequent and user interested items. Existing association mining methods [2-3] use the support-confidence framework [4] in the discovery of user-interested rules. However, this framework is not sufficient for measuring the utility of item sets. In finding the utility of item sets [5], the traditional support-confidence framework is enhanced for measuring the semantic relations among the items which takes the semantic measure of the rule i. e the importance of the item is considered in the rule. Frequent item set mining (FIM) [6] is one of the most important data mining task and it is popular in wide range of real life applications. The FIM discovers frequent itemsets using either Apriori or FP-growth [7] from a given transaction database, so frequently itemsets are appeared in results of transactions. Apriori and FP-growth methods generated the frequent itemsets without considering the profit of itemsets. It is emerging that; we can also consider the importance of frequent itemsets in terms of either a profit or utility. High Utility itemsets refers to a set of frequent items with high utility. High Utility itemsets mining (HUIM) [8] methods are playing a vital role in producing the set of high utility frequent item sets [9]. Association rule mining system is one of the popular methods for discovering of knowledge discovery about finding the relationships among the items. Aim of traditional association rule mining (or Apriori) is to discover the frequent itemsets, which defines the itemsets of each transaction in the transactional database. One of the limitation of this mining system is not concerned the other factors of

Mining of Frequent Itemsets and Utility from Operational Data using Data Mining Technique

2015

Frequent Itemset Mining method generate the frequent patterns. The frequency of an itemset may not be a sufficient indicator of interestingness, because it only reflects the number of transactions in the database that contain the itemset. It does not reveal the utility of an itemset, which can be measured in terms of cost, profit, or other expressions of user preference. In this research, maximize profit, the itemset utilities should be decided by the quantity of items sold and the unit profit on these items. In proposed system, an algorithm named Utility Model-Growth for mining high utility itemsets from transaction databases are used. Utility Model-Tree maintains the information of high utility itemsets. The mining performance is enhanced significantly since both the search space and the number of candidates are effectively reduced.

IRJET- Study of Algorithms for Mining High Utility Itemsets

IRJET, 2020

Data mining techniques are applied for finding meaningful information and patterns from the large database. The traditional frequent itemset mining (FIM) algorithm generate large number of frequent itemset considering only the occurrence aspect of itemset It does not take into consideration the utility aspect of quantity and profit of item purchased. Hence an extension to FIM, High utility itemsets (HUIs) mining is emerging in information mining, which considers finding all itemsets having a utility meeting a user-specified minimum utility threshold. High-utility itemset mining (HUIM), aims to find a complete set of itemsets having high utilities in a given dataset. High average-utility itemset mining (HAUIM) is a variation of HUIM. HAUIM provides an alternative measurement named the average-utility to discover the itemsets by taking into consideration both of the utility values and lengths of itemsets. Efficient algorithms named TKU (mining Top-K Utility itemset) and TKO (mining Top-K utility itemset in One phase) are used in HUIM. A pattern growth approach is specified for efficiently mining of HAUIs. This paper studies the different algorithms for mining of high utility itemset.

A SURVEY PAPER ON HIGH UTILITY ITEMSETS MINING

An important data mining task that has received considerable research attention in recent years is the discovery of association rules from the transactional databases. Recently, Utility mining plays a vital role in data mining. To discover high utility itemset from transactional database means discovering item sets with high profits. In this survey paper, we discuss about various methods and algorithms which were used for recovering high utility itemsets from a large database without losing large amount of information.We present different kind of algorithm such as CHUD(Closed High Utility Itemset Discovery) for mining closed itemset and further a method called DAHU which discovers all high utility itemsets from a result generated after applying CHUD algorithm .Itemset mining has a wide range of applications in biomedical applications, retail stores, super market etc.

High Utility ITEMSET Mining from Large Database

Frequent itemset mining is one of the main problems in data mining. It has practical importance in a wide range of application areas such as decision support, Web usage mining, bioinformatics, etc. A number of relevant algorithms have been proposed in recent years for the fast access of data from the database. Mining high utility itemsets from a large database refers to the discovery of itemsets with high utility like profits. The proposed work is to mine the high utility items from the large database. The traditional association rule mining algorithm is used to find out the frequently occurring patterns of item sets. Apriori algorithm is used to find the high utility itemset. Data about the products are collected and stored in a database. Whenever customers buy the same product repeatedly the frequent pattern is formed and the infrequent items are separated. The high utility itemset is based on the user-specified utility threshold or it is a low-utility itemset. Admin maintain the entire system process like workers details, user details, product sales, raw materials. Admin can generate report based on the product sales. Admin can generate the Apriori products based on the threshold value. Admin can generate the graph for the frequently purchased products.

Various Research Opportunities in High Utility Itemset Mining

International Journal of Recent Technology and Engineering

Pattern mining is a technique, which discovers interesting, hidden, unpredicted and useful patterns of data from the database. Most of the research work in pattern mining has been focused on the traditional way of Frequent Itemset Mining (FIM) and Association Rule Mining (ARM) for patterndiscovery. Patterns in frequent itemset mining are based on the occurrence frequency of items. Although frequent pattern mining is useful, the assumption that ‘frequent patterns are interesting,’ doesn’t hold for numerous applications. High Utility Itemset Mining (UIM) overcomes this limitation of frequent itemset mining. The aim of HUIM is to find the patterns based on a utility function where the utility can be measured in terms of revenue, profit, weight, frequency, interestingness or time spent on some webpage, etc. Mining patterns with high utility can be seen as a generalization of FIM where the transaction database is the input and every item is having a utility factor representing its import...

Mining utility-oriented association rules: An efficient approach based on profit and quantity

2011

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 association patterns. In this paper, we propose an efficient approach based on weight factor and utility for effectual mining of significant association rules. Initially, the proposed approach makes use of the traditional Apriori algorithm to generate a set of association rules from a database. The proposed approach exploits the anti-monotone property of the Apriori algorithm, which states that for a k-itemset to be frequent all (k-1) subsets of this itemset also have to be frequent. Subsequently, the set of association rules mined are subjected to weightage (W-gain) and utility (U-gain) constraint...

A Review of some Popular High Utility Itemset Mining Techniques

IJSRD, 2013

Data Mining can be defined as an activity that extracts some new nontrivial information contained in large databases. Traditional data mining techniques have focused largely on detecting the statistical correlations between the items that are more frequent in the transaction databases. Like frequent item set mining, these techniques are based on the rationale that item sets which appear more frequently must be of more importance to the user from the business perspective. In this thesis we throw light upon an emerging area called Utility Mining which not only considers the frequency of the item sets but also considers the utility associated with the item sets. The term utility refers to the importance or the usefulness of the appearance of the item set in transactions quantified in terms like profit, sales or any other user preferences. In High Utility Item set Mining the objective is to identify item sets that have utility values above a given utility threshold. In this thesis we present a literature review of the present state of research and the various algorithms for high utility item set mining.

Overview of Itemset Utility Mining and its Applications

International Journal of Computer Applications, 2010

An emerging topic in the field of data mining is Utility Mining. The main objective of Utility Mining is to identify the itemsets with highest utilities, by considering profit, quantity, cost or other user preferences. Mining High Utility itemsets from a transaction database is to find itemsets that have utility above a user-specified threshold. Itemset Utility Mining is an extension of Frequent Itemset mining, which discovers itemsets that occur frequently. In many real-life applications, high-utility itemsets consist of rare items. Rare itemsets provide useful information in different decision-making domains such as business transactions, medical, security, fraudulent transactions, retail communities. For example, in a supermarket, customers purchase microwave ovens or frying pans rarely as compared to bread, washing powder, soap. But the former transactions yield more profit for the supermarket. Similarly, the high-profit rare itemsets are found to be very useful in many application areas. For example, in medical application, the rare combination of symptoms can provide useful insights for doctors [21]. A retail business may be interested in identifying its most valuable customers i.e. who contribute a major fraction of overall company profit[10]. Several researches about itemset utility mining were proposed. In this paper, a literature survey of various algorithms for high utility rare itemset mining has been presented.

An Efficient System for Frequent Itemset Mining Using Optimization Based Apriori Algorithm

2021

Web log is the most valuable input of the web analysis with Web Usage Mining (WUM). Web log information is collected from the server, client and proxy server. Web usage Mining is a kind of the web analysis, pre-processing stage in WUM consists the following Data Cleaning, user identification, session identification and path completion (path added). In this research article focuses the path completion part. After completing preprocessing stage, frequent itemset mining is carried out using Apriori algorithm. In this paper, the main area of focus is to optimize the rules/feature, which is generated by Association Rule Mining with various optimization methods. The key idea of the proposed work is to find closely related features using association rule mining method. Apriori algorithm is used to find closely related attributes using support and confidence measures. From closely related attributes a number of association rules are mined. Among these rules, only few related with the desira...