Research on Association Rule Mining 2012.pdf (original) (raw)

A Novel Approach for Association Rule Mining

ijetae.com

Abstract—Data mining (DM) is a non-trivial extraction of novel, implicit, and actionable knowledge from large data sets. For large databases, the research on improving the mining performance and precision is necessary; so many focuses of today on association rule ...

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.

A Survey on Association Rule Mining

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.

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.

IJERT-Review on Association Rule Mining: A Survey

International Journal of Engineering Research and Technology (IJERT), 2014

https://www.ijert.org/review-on-association-rule-mining-a-survey https://www.ijert.org/research/review-on-association-rule-mining-a-survey-IJERTV3IS042032.pdf Association rule mining plays important role in the field of data mining. Association rule mining is a technique that helps to prepare the way to improve the mining technique. It is a method to discover relationships among variables in the database. Association rule basically divided into two different parts (a) an antecedent and (b) a consequent. In association rule mining different types of approaches and algorithms have been designed but it is very important to know which approach is best and suitable for association rule. So In this paper, we present a complete survey on different algorithms and approaches used in association rule mining in different domain.

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.

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 .

A Study and Analysis of Association Rule Mining Algorithms In Data Mining

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.

ARMICA-Improved: A New Approach for Association Rule Mining

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 ...

Comparative Survey on Association Rule Mining Algorithms

International Journal of Computer Applications, 2013

Association rule mining has become particularly popular among marketers. In fact, an example of association rule mining is known as market basket analysis. The task is to find which items are frequently purchased together. This knowledge can be used by professionals to plan layouts and to place items that are frequently bought together in close proximity to each other, thus helping to improve the sales. Association rule mining involves the relationships between items in a data set. Association rule mining classifies a given transaction as a subset of the set of all possible items. Association rule mining finds out item sets which have minimum support and are represented in a relatively high number of transactions. These transactions are simply known as frequent item sets. The algorithms that use association rules are divided into two stages, the first is to find the frequent sets and the second is to use these frequent sets to generate the association rules. In this paper the applications, merits and demerits of these algorithms have been studied. This paper discusses the respective characteristics and the shortcomings of the algorithms for mining association rules. It also provides a comparative study of different association rule mining techniques stating which algorithm is best suitable in which case.