An Evaluation of Association Rule Mining for Market Basket Analysis

Integrated Intelligent Research (IIR)

International Journal of Business Intelligents Volume: 05 Issue: 01 June 2016 Page No.90-92 ISSN: 2278-2400

An Evaluation of Association Rule Mining for Market Basket Analysis S.Sivakumar

Assistant Professor, Dept of MCA,SRM Arts and Science College,Chennai

Abstract-Association Rule Mining (ARM) is now playing a very important role for identifying market strategies and to find out what are the items purchased consistently by various customers. ARM is a data mining function that discovers the probability of the co-occurrence of items in a collection. It is mainly used for focusing on the purchase of the items, to improve the sales strategy consistently with the help of sales transaction data and to maintain the inventory level positively.

In this paper, the main objective is on the survey of association rule mining for market basket analysis. This paper provides vast detail about association rule mining which will be useful to minimize complexities of market basket analysis.

The chapter 2 presents Association rule mining and their implementation. ARM is also called Market Basket Analysis (MBA). This mainly concentrates the buying habits of a customer by identifying association between the different items the customer place in their shopping baskets.Section 1 gives the introduction of Association Rule Mining and their importance, whereas section 2 presents an overview of Market Basket Analysis and their necessity. Section 3 depicts the literature survey of MBA. Section 4 covers various Associations rule mining algorithms and finally conclusion are depicted by section 5.

II.

Keywords-Data Mining, Association Rule Mining, Market Basket Analysis, The Apriori algorithm, FP Growth algorithm.

I.

ASSOCIATION RULE MINING

Association rule mining is used for analyzing and predicting customer behavior from a large database. It plays an important role in shopping basket data analysis, product clustering, catalog design and store layout. This chapter discusses the importance of Association Rule and their implementation. An association rule [10] is defined as an implication of the form x=>y, where x is called antecedent and y is called consequent.

X and Y are set of items and the rule represents if item x are bought, customer are buy y.

INTRODUCTION

Today, the business organizations are eager to stand out their customers and provide more services and experiences. To be current competitive and stay ahead of line, business organizations are mostly closing on customer data collection for insight into trends and customer behavior. However, the information being collected from various sources is developing into large quantities of data at a rapid pace. Generating insight from high velocity and volume requires business organizations to have a technology landscape that can scale and plays at the speed of business demands [7,9].Data mining aims to discover hidden information from massive sets of data [5, 6, 13, 14] and involves various methods of the intersection of artificial intelligence, machine learning, statistics and database systems.

Information which is used to increase revenue, cuts costs or both. Data Mining is the analysis step of the “knowledge discovery in databases� process. It uses previous statistical data analysis to derive patterns and trends that exist in data nd to predict future behavior. Data mining techniques are widely used in various applications like Financial Data Analysis, Retail industry, Telecommunication Industry, Biological Data Analysis, Intrusion detection, Data warehouses, Visualization and Domain specific knowledge, etc. Association rule mining [ARM] is an important application of data mining. ARM is a method for discovering interesting relations between data in large databases. Most of the business organizations are concerned in association rule mining from their databases, due to the increase of large collection of data items in databases.

The table 1 shows database with 8 transactions and 4 items from the supermarket domain.

Table 1: Transaction details Transacti on ID 1 2 3 4 5 6 7 8

MIL K Yes No No Yes Yes No No Yes

BREA D Yes No No Yes No Yes No No

BUTTE R Yes Yes No Yes Yes No No No

BEE R No No Yes No Yes No Yes No

From the table 1, Where yes represents the presence of item and no represents the absence of an item in that transaction. An association rule is defined as follows {butter,bread}=>{milk} which means customer would definitely purchased milk if they had purchased bread and butter.Support and Confidence are the measurement of the ARM. The ARM support explains the 90

Integrated Intelligent Research (IIR)

International Journal of Business Intelligents Volume: 05 Issue: 01 June 2016 Page No.90-92 ISSN: 2278-2400 classification technique. It creates association rule mining based classifier with the help of classification association rule (CAR). CARM algorithm is not considered practically, a similar set of CAR’s from data and a classifier is given as an ordered list of cars, established on a selected rule ordering strategy. This paper introduces a new technique rule weighing schema, called class-item score rule weighing for providing the accuracy of classification.

number of times the customer purchases the milk products whereas confidence defines as the accuracy of the rule. Based on table 1, itemset {milk,bread} has a support of 2/6=0.33(33%) whereas the association rule {butter,bread} => {milk} has a confidence of 0.3/0.3=1.0(100%). Support is used for a minimum threshold is supplied to find all frequent items in a database. Confidence gives a minimum confidence constraint is applied to those frequent item sets in order to form rules.

IV. III.

ALGORITHMS FOR ARM

LITERATURE SURVEY 4.1 The Apriori algorithm

Tmka [1] proposed data mining methods for market basket analysis. This author takes six sigma technique for market basket analysis implementation. Six sigma techniques provides various statistics techniques of define(D), measure(M),analysis(A),improve(I) and control(C) for improving the results of MBA and modifying the process of sigma performance level. With the representation of products dependence, web plot is used.This six sigma method might be used to target special offers which indirectly improves the performance level since this can be useful in spending money which targets a specific customer group. Vo [8] has presented a model called mining traditional association rules using frequent itemsets lattice. Various methods have to be proposed for mining frequent itemsets. With the development of an application of lattice in mining rule, the time will be decreased gradually for mining rules. Construction of frequent itemsets lattice and mining association rules from lattice are the stages of mining frequent itemsets. With the execution of association rule mining in lattice, the parent-child relationship is applied.

The result examines that more efficient that the straight mining from frequent itemsets. In future, the research can propose a method for considering lattice in mining non redundant rules to reduce the number of rules by rejecting redundant rules and reducing the time of mining rules.

This algorithm [11] finds the frequent individual items from the database and extend them to huge sets. It uses a bottom-up approach, where recurrent subsets are expanded one item at a time and group of candidates are examined against the data.

This algorithm will terminate when there is no further successful extensions are found. There are many difficulties by applying Apriori algorithm against large databases which scans the database many times and to take time complexities are high from frequent item in the larger set length of the circumstances.

4.2 FP(Frequent Pattern) growth algorithm This algorithm [11] takes a prefix-tree data structure for storing compressed and crucial information of frequent items from the database. It repeatedly creates conditional parameter and from the FP tree structure and takes them to produce the complete set of frequent patterns. The divide and conquer strategy are here applied for decomposing data items from database and the mining task. It applies frequent pattern method to evade the costly process of candidate generation and testing opposed to Apriori algorithm.

V.

Pei [2] has suggested a model of mining association rules based on Apriori algorithm and application. Mining association rule has acted a significant subject of data mining research.

This paper also takes mining association rule algorithm and mining frequent itemsets algorithm on improved measurement of system. The measure of ARM’s support and confidence and interestingness useful rules and losing useful rules.Barthik [4] presents a paper association based classification for relational data model and its use in web mining. Classification ARM is a technique for providing better accuracy and human understandable classification scheme. This paper is to propose the modification of the basic classification with help of classification method and used for extracting data from web pages. This work can be extended the method with the processing text contained in visual element, which leads to improved quality of classification.Wang [3] suggested a model called a novel rule weighting approach in classification association rule mining (CARM). CARM [12] is a new

CONCLUSION

Association rule mining plays a very important role in Data Mining Techniques. From the collection of large amount of information from databases, Physical analysis is risky and difficult. Accuracy and efficiency are important task in such situations. So it is necessary to apply association rule mining.

This paper covers various association rule mining algorithms which explains various advantages and disadvantages thus gives us how these algorithms are used for market basket analysis. From the Association rule mining algorithms, the Apriori algorithm is better than any other algorithm for producing the results from the databases and has some difficulties. Due to the complexities of such algorithms, there is a need to improve association rule algorithms for providing better accuracy and time complexities.

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