A Novel Approach for Association Rule Mining

Association Rule Mining is one of the important areas of research, receiving increasing attention. It is an essential part of Knowledge Discovery in Databases (KDD). The scope of Association Rule Mining and KDD is very broad. Over the last fifteen years it has been developed at a dynamic rate. Although it has been emerged as a new technology but Association Rule Mining is still in a stage of exploration and development. In this paper we present a survey of research work carried by different researchers since its beginning. Of course, a single article cannot be a complete review of all the research work, yet we hope that it will provide a guideline for the researcher in interesting research directions that have yet to be explored.

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.

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.

In recent years, Association Rule Discovery has become a core topic in Data Mining. It attracts more attention because of its wide applicability. Association rule mining is normally performed in generation of frequent itemsets and rule generation in which many researchers presented several efficient algorithms. This paper aims at giving a theoretical survey on some of the existing algorithms. The concepts behind association rules are provided at the beginning followed by an overview to some of the previous research works done on this area. The advantages and limitations are discussed and concluded with an inference.

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.

2017

With increasing in amount of available data, researchers try to propose new approaches for extracting useful knowledge. Association Rule Mining (ARM) is one of the main approaches that became popular in this field. It can extract frequent rules and patterns from a database. Many approaches were proposed for mining frequent patterns; however, heuristic algorithms are one of the promising methods and many of ARM algorithms are based on these kinds of algorithms. In this paper, we improve our previous approach, ARMICA, and try to consider more parameters, like the number of database scans, the number of generated rules, and the quality of generated rules. We compare the proposed method with the Apriori, ARMICA, and FP-growth and the experimental results indicate that ARMICA-Improved is faster, produces less number of rules, generates rules with more quality, has less number of database scans, it is accurate, and finally, it is an automatic approach and does not need predefined minimum ...

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.

The data mining is a technology that has been developed rapidly. It is based on complex algorithms that allow for the segmentation of data to identify pattern and trends, detect anomalies, and predict the probability of various situational outcomes. The raw data being mined may come in both analog and digital formats depending on the data sources. There are many trends that are available in data mining some of the new trends are Distributed Data Mining (DDM), Multimedia Data Mining, Spatial and Geographic Data Mining, Time Series and Sequence Data Mining, Time Series and Sequence Data Mining. This paper is based on Association rule mining. In the field of association rule mining, many algorithms exist for exploring the relationships among the items in the database. These algorithms are very much different from one another and take different amount of time to execute on the same sets of data. In this paper, a sample dataset has been taken and various association rule mining algorithms namely Apriori, FP-Growth, Tertius have been compared. The algorithms of association rule mining are discussed and analyzed deeply. The main objective of this paper is to present a review on the basic concepts of ARM technique and its algorithms.

2018

Data mining (DM) techniques is the set of algorithms that helps in extracting interesting patterns and previously unknown facts from larger volume of databases. Todays ever changing customer needs, fluctuation business market and large volume of data generated every second has generated the need of managing and analyzing such a large volume of data. Association Rule mining algorithms helps in identifying correlation between two different items purchased by an individual. Apriori Algorithm and FP-Growth Algorithm are the two algorithms for generating Association Rules. This paper aims at analyze the performance of Apriori and FP-Growth based on speed, efficacy and price and will help in understanding which algorithm is better for a particular situation. https://journalnx.com/journal-article/20150659

Abstract — Data mining is an emerging field that comprises of various functions like classification, association rule mining, clustering, and outlier analysis. Association rule mining is a major, interesting and extremely studied function of data mining. Association rule mining identifies the correlation between different itemsets and find frequent and interesting rules. Frequent itemset mining is very common first step in considering datasets through wide range of applications. There have been proposed some methods in literature which scan database twice or more times to find approximate frequent patterns and frequent itemsets. Scanning database again and again makes mining process tedious and slow. The traditional approaches needs that every item in itemset happens in each supporting transaction. Yet the actual data has noise (meaningless data) and in existence of a noise, outdated itemset mining procedures might not be able to identify related frequent itemset(s). We have proposed a method in this paper that solved above mentioned problems. It scans database only once and makes mining fast and efficient. Our proposed method used technique named Fault Tolerance to handle noisy data and replaced database with a tree like structure. We are unaware of any technique yet introduced that can find approximate frequent itemset with only one scan of database. Further, our proposed method has an advantage on traditional Apriori and frequent pattern (FP) Tree) method as for as scanning and infrequent candidate generation are concerned. Keywords: Approximate pattern; frequent pattern; Apriori; fault tolerance; FP-Tree; FT-Apriori; AFI-FP