A Survey on Utility Mining Methods 2PUF, IHUP, FUFM

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.

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.

2018

Discovering itemsets with high utility like profit from database is known as High Utility Itemset mining. In many real time applications such as retail marketing and Web service the High utility itemsets mining is useful in decisionmaking process. Efficient Mining of High utility itemsets plays a very important role in many real time applications and is a vital research issue in data mining area. The existing high utility mining algorithm degrade the performance takes much time to generate large number of candidate itemsets and to find utility value of all candidate itemsets In this research article, the time and space complexity of UP Growth and UP Growth+ algorithm have been reduced by 1 International Journal of Pure and Applied Mathematics Volume 118 No. 17 2018, 257-272 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu Special Issue ijpam.eu

NeuroQuantology, 2022

The data with the advancement of information technology are expanding on regular routine. The data mining strategy has been applied to different fields. The intricacy and execution time is the main considerations saw in existing data mining procedures. With the fast advancement of database technology, numerous data stockpiling increments and data mining technology has become increasingly significant and extended to different fields as of late. Affiliation rule mining is the most dynamic examination procedure of data mining. Data mining technology is utilized for possibly valuable information extraction and knowledge from large data sets. The results show that the precision proportion of the introduced procedure is high contrasted with other existing methods with a similar review rate

mining high utility pattern is an important research area in data mining. Several business applications have been found to benefit from the discovery of high utility itemsets and association rules from transaction databases. A comprehensive survey and study of various methods in existence for high utility itemset mining, association rule mining with utility considerations have been presented in this paper. In these, the various mining techniques are used such as Incremental Mining of High Utility Patterns, High Average-Utility Patterns with Multiple Minimum Average-Utility Thresholds, Using Bio-Inspired Algorithms, Algorithm for Incremental and Interactive High Utility Itemset Mining, using Temporal-Based Fuzzy Utility Mining. However there are some issues that need to resolve these are discussed in this paper. Various algorithms are studied for the comparative analysis of high utility pattern mining algorithms and new method is proposed.

— The term data mining is often used to apply to the two separate processes of knowledge discovery and prediction. Knowledge discovery provides explicit information that has a readable form and can be understood by a user. Forecasting, or predictive modeling provides predictions of future events and may be transparent and readable in some approaches (e.g. rule based systems) and opaque in others such as neural networks. Moreover, some data mining systems such as neural networks are inherently geared towards prediction and pattern recognition, rather than knowledge discovery. Utility item set mining is addition to the frequent pattern mining. The goal of high utility item set mining is to find all item sets that give utility greater or equal to the user specified threshold. The deficiency of this approach is that it does not consider the statistical aspect of item sets. Utility-based measures should incorporate user-defined utility as well as raw statistical aspects of data. Consequently, it is meaningful to define a specialized form of high utility item sets, utility-frequent item sets which are a subset of high utility item sets as well as frequent item sets. In this paper we proposed an efficient approach to mine high utility items form transactional records.

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.

Information Mining, additionally called learning Discovery in Database, is one of the most recent research region, which has developed in light of the Tsunami information or the surge of information, world is confronting these days. It has responded to the call to create methods that can assist people with discovering valuable patterns in monstrous information. One such significant method is utility mining. Visit thing set mining attempts to find thing set which are every now and again show up in exchange database, which can be find based on help and certainty estimation of various item set. Utilizing successive item set mining idea as a base, numerous scientists have likewise proposed diverse new idea on utility based mining of item set. This paper exhibits an investigation of different approaches utilized for mining high utility thing sets from an utility informational collection. This paper also presents a hash map based technique for mining all high utility item sets from a transaction data set and profit data set. The proposed technique is efficient in comparison to existing technique

2014

Data mining has been around and all enterprises in the real world need it in order to make well informed decisions. The reason behind this is that analyzing huge data is not possible manually. For mining high utility item sets from databases many techniques came into existence. The discovery of item sets with high utility like profits is referred by mining high utility item sets from a transactional database. A number of data mining algorithms have been proposed, for high utility item sets the problem of producing a large number of candidate item sets is incurred. The mining performance is degraded by such a large number of candidate item sets in terms of execution time and space requirement. There are many Problems Occurs when the database contains lots of long transactions or long high utility item sets. Internet purchasing and transactions is increased in recent years, mining of high utility item sets especially from the big transactional databases is required task to process man...

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.