A Study on Association Rules (original) (raw)

Association Rules in Data Mining

Data mining is motivated by the decision support problem faced by most large retail organizations. Association rule mining is finding frequent patterns, associations, correlations or casual structures among sets of items or objects in transactional databases, relational databases and other information repositories. It has various applications including market-basket data, analysis, cross marketing, catalogue design, and loss-leader analysis. For example, 98% of customers that purchase tires and auto accessories also get automotive services done. Finding all such rules is valuable for crossmarketing and attached mailing applications. In this paper presentation we will analyses the various data association rules and develop an insight into the implementation of these rules for better sales of a company. Moreover in data mining association rules are useful for analyzing and predicting customer behavior. We will also throw a light on Apriori Algorithm, which is probably the best known algorithm for learning association rules.Apriori is designed to operate on databases containing transactions. For example: Collection of items bought by customers or detail of a website frequentation. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets, as long as those item sets appear sufficiently often in the database.

Data Mining of Association Rules and the Process of Knowledge Discovery in Databases

2002

This book presents papers describing selected projects on the topic of data mining in fields like e-commerce, medicine, and knowledge management. The objective is to report on current results and at the same time to give a review on the present activities in this field in Germany. An effort has been made to include the latest scientific results, as well as lead the reader to the various fields of activity and the problems related to them.

Implementation of Algorithms Related to Association Rules in Large Databases

IAEME PUBLICATION, 2013

Data mining is involved with the use of advanced data analysis tools to find out new, suitable patterns and project the relationship among the patterns which were not known prior. In data mining, association rule learning is a more suitable method for ascertaining new relations between variables in large databases. The objective the technique focuses on the formulation of association rules. The discovery of association relationships among large amount of transactions as well as data may be vital for making multi decisions. Numerous algorithms are available to discover association rules. Usually quite few algorithms depend on the use of minimum support whereas other algorithms are inclined to highly interrelated items. In this paper it is intended to describe the association rule algorithms and a comparison of two algorithms representative of these approaches, e.g. support and confidence based approaches.

COMPARATIVE STUDY OF EFFECTIVE PERFORMANCE OF ASSOCIATION RULE MINING IN DIFFERENT DATABASES.pdf

CIIT- Data Mining and Knowledge Discovery, 2018

Data mining practices expert procedures and methods to identify the tendencies and profiles concealed in data. Mining is an iterative process in a sequence. Different sources of data are stored in different databases. The mining depends on databases. This research is for various association rule mining applications of different databases. There are different databases in practice like large database, distributed database, medical database, relational database, spatial database. The process of mining these databases are carried out by different data mining techniques. For making decisions, association rule is most essential. They are associated with association rule mining techniques.

Real World Association Rule Mining

British National Conference on Databases, 2002

Across a wide variety of fields, data are being collected and accumulated at a dramatic pace, and therefore a new generation of techniques has been proposed to assist humans in extracting usefull information from the rapidly growing volumes of data. One of these techniques is the association rule discovery, a key data mining task which has attracted tremendous interest among data mining researchers. Due to its vast applicability, many algorithms have been developed to perform the association rule mining task. However, an immediate problem facing researchers is which of these algorithms is likely to make a good match with the database to be used in the mining operation. In this paper we consider this problem, dealing with both algorithmic and data aspects of association rule mining by performing a systematic experimental evaluation of different algorithms on different databases. We observed that each algorithm has different strengths and weaknesses depending on data characteristics. This careful analysis enables us to develop an algorithm which achieves better performance than previously proposed algorithms, specially on databases obtained from actual applications.

Association Rules Mining for Business Intelligence

Business Intelligence (BI) is any information derived from analytics of existing data that can be used strategically in the organization. Data Mining is a subset of BI or a means/process of deriving BI from data using statistical modeling of the data. It can be used to find relationships/correlations between the various data elements captured which can be used to improve business performance or at least understand what is happening better. With the rapid exponential growth in size and number of available Databases in commercial, industrial, administrative and other applications, it is mandatory and important to examine how to extract knowledge from voluminous data. Mining Association rules in transactional or relational databases has recently attracted a lot of attention in database communities. The task is to derive a set of strong association rules in the form of “A1^….^ m=>B1^…^Bm” where Ai(for i €{1,2,.....m}) and Bi(for j € {1,2,.....,n}) are set of attribute-values, from the relevant data sets in a databases.

ASSOCIATION RULE MINING: A DATA PROFILING AND PROSPECTIVE APPROACH

INTERNATIONAL JOURNAL OF CURRENT ENGINEERING AND SCIENTIFIC RESEARCH (IJCESR), 2016

The Main objective of data mining is to find out the new, unknown and unpredictable information from huge database, which is useful and helps in decision making. There are number of techniques used in data mining to identify frequent pattern and mining rules includes clusters analysis, anomaly detection, association rule mining etc. In this paper we discuss the main concepts of association rule mining, their stages and industries demands of data mining. The pitfalls in the existing techniques of association rule mining and future direction is also present.

Mining association rules between sets of items in large databases

Sigmod Record, 1993

We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.

IJERT-A Novel Association Rule Mining in Large Databases

International Journal of Engineering Research and Technology (IJERT), 2013

https://www.ijert.org/a-novel-association-rule-mining-in-large-databases https://www.ijert.org/research/a-novel-association-rule-mining-in-large-databases-IJERTV2IS3629.pdf One of the core topics of data mining is mining association rules in large databases. The correct and appropriate decision made by decision makers is the advantage in discovering these associations. The key process in association rule mining is discovering frequent item sets. Main challenges in developing association rules mining algorithms are the large number of rules generated that makes the algorithms inefficient and makes it complicated for end users to comprehend the generated rules. It is because of the many traditional association rule mining approaches adopt an iterative technique to discover association rule, which requires many calculations and a difficult transaction process. Furthermore, the existing mining Due to high and repeated disk access overhead the existing algorithms cannot perform efficiently. By keeping this thing in mind, in this paper we present a novel association rule mining approach that can efficiently find the association rules in large databases. By using the conventional Apriority approach with added features to improve data mining performance has been derived in the proposed approach. We have performed many experiments and differentiated the performance of our algorithm with existing algorithms found in the literature. Experimental results show that our approach can quickly and easily discover the frequent item sets and effectively mine potential association rules .