A Survey on Sequential Rule Mining Techniques (original) (raw)

Efficient Analysis of Pattern and Association Rule Mining Approaches

International Journal of Information Technology and Computer Science, 2014

The process of data mining produces various patterns from a given data source. The most recognized data mining tasks are the process of discovering frequent itemsets, frequent sequential patterns, frequent sequential rules and frequent association rules. Numerous efficient algorithms have been proposed to do the above processes. Frequent pattern mining has been a focused topic in data mining research with a good number of references in literature and for that reason an important progress has been made, varying from performant algorithms for frequent itemset mining in transaction databases to complex algorithms, such as sequential pattern mining, structured pattern mining, correlation mining. Association Rule mining (ARM) is one of the utmost current data mining techniques designed to group objects together from large databases aiming to extract the interesting correlation and relation among huge amount of data. In this article, we provide a brief review and analysis of the current status of frequent pattern mining and discuss some promising research directions. Additionally, this paper includes a comparative study between the performance of the described approaches.

A Survey on Association Rule Mining Algorithms for Frequent Itemsets

— These days many current data mining tasks are accomplished successfully only in discovery of Association rule. It appeals more attention in frequent pattern mining because of its wide applicability. Many researchers successfully presented several efficient algorithms with its performances in the area of rule generation. This paper mainly assembles a theoretical survey of the existing algorithms. Here author provides the considered Association rule mining algorithms by beginning an overview of some of the latest research works done on this area. Finally, discusses and concludes the merits and limitation.

Survey on Incremental Association Rule Mining to Find Frequent Itemsets

2015

Mining frequent Itemsets has proved to be very difficult because of its computational complexity. But, , it has gained a lot of popularity due to the usefulness of association rules, despite having huge processing cost. This paper provides a comprehensive survey on the state-of-art algorithms for association rule mining, specially when the datasets used for rule mining are dynamic. When new data are added to a original dataset it may lead to additional rules or to modification of some existing rules. To find the association rules from the whole (old as well as new) dataset will be wastage of time only if the process is restarted from the beginning. Several algorithms have been developed to attend this important issue of the association rule mining problem. This paper analyzes some of the algorithms to tackle the incremental association rule mining problem.

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 .

KSD an Efficient Algorithm for Association Rule Mining

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

A Survey on Approaches for Mining Frequent Itemsets

Data mining is gaining importance due to huge amount of data available. Retrieving information from the warehouse is not only tedious but also difficult in some cases. The most important usage of data mining is customer segmentation in marketing, shopping cart analyzes, management of customer relationship, campaign management, Web usage mining, text mining, player tracking and so on. In data mining, association rule mining is one of the important techniques for discovering meaningful patterns from large collection of data. Discovering frequent itemsets play an important role in mining association rules, sequence rules, web log mining and many other interesting patterns among complex data. This paper presents a literature review on different techniques for mining frequent itemsets.

An Efficient Mining Algorithm for Closed Frequent Itemsets and its Associated Data

2020

Database is a repository of information. Retrieving automatic patterns from the database provide the requisite information and are in great demand in various domains of science and engineering. The effective pattern mining methods such as pattern discovery and association rule mining have been developed and find its applicability in a wide gamut ranging from science to medical to military and to engineering applications. Contemporary methods of retrieval such as pattern discovery and association rule mining algorithms are useful only for retrieving the data. The limitations of using these techniques are that they are unable to provide a complete association and relationship among the diverse patterns that is retrieved. This paper attempts a solution to the above limitation by designing a new algorithm (CFIM) which generates closed frequent patterns and its associated data concurrently. CFIM makes explicit the relationship between the patterns and its associated data.

Co-occurrence of frequent itemsets in Association Rule Mining

International Journal

In this paper, we have proposed an algorithm for Association rule mining to find frequent itemsets .This approach is based on Frequent Pattern Tree to find co-occurrence of frequent itemset which will ultimately reduce the scanning of the database resulting in lesser CPU time and is more efficient than the existing FP growth approach. In this approach a relatively small tree is created for each frequent item based on the user defined minimum support so the memory required by data structure is comparatively very low as huge memory is required for storing itemset in data structure in previous approach and also a very simple and non recursive mining process is done.

Intelligent computation for association rule mining

2005

Although there have been several encouraging attempts at developing SQL-based methods for data mining, simplicity and efficiency still remain significant impediments for further development. In this paper, we develop a fixpoint operator for computing frequent itemsets and demonstrate three query paradigm solutions for association rule mining that use the idea of least fixpoint computation. We consider the generate-and-test and the frequent-pattern growth approaches and propose an novel method to represent a frequent-pattern tree in an object-relational table and exploit a new join operator developed in the paper. The results of our research provide theoretical foundation for intelligent computation of association rules and could be useful for data mining query language design in the development of next generation of database management systems.

CBW: an efficient algorithm for frequent itemset mining

37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the, 2004

Frequent itemset generation is the prerequisite and most time-consuming process for association rule mining. Nowadays, most efficient Apriori-like algorithms rely heavily on the minimum support constraint to prune a vast amount of non-candidate itemsets. This pruning technique, however, becomes less useful for some real applications where the supports of interesting itemsets are extremely small, such as medical diagnosis, fraud detection, among the others. In this paper, we propose a new algorithm that maintains its performance even at relative low supports. Empirical evaluations show that our algorithm is, on the average, more than an order of magnitude faster than Apriori-like algorithms.