Association Rules In Large Databases Research Articles

AN EFFICIENT DATA MINING METHOD TO FIND FREQUENT ITEM SETS IN LARGE DATABASE USING TR- FCTM

Mining association rules in large database is one of most popular data mining techniques for business decision makers. Discovering frequent item set is the core process in association rule mining. Numerous algorithms are available in the literature to find frequent patterns. Apriori and FP-tree are the most common methods for finding frequent items. Apriori finds significant frequent items using candidate generation with more number of data base scans. FP-tree uses two database scans to find significant frequent items without using candidate generation. This proposed TR-FCTM (Transaction Reduction- Frequency Count Table Method) discovers significant frequent items by generating full candidates once to form frequency count table with one database scan. Experimental results of TR-FCTM shows that this algorithm outperforms than Apriori and FP-tree.

Mining of Association Rules on Large Database Using Distributed and Parallel Computing

Now days due to rapid growth of data in organizations, extensive data processing is a central point of Information Technology. Mining of Association rules in large database is the challenging task. An Apriori algorithm is widely used to find out the frequent item sets from database. But it will be inefficient in case of large database because it will require more I/O load. Later drawback of the Apriori algorithm is overcome by many algorithms / parallel algorithms (model) but those are also inefficient to find frequent item sets from large database with less time and with great efficiency. Hence hybrid architecture is proposed which consists of integrated distributed and parallel computing concept. The main idea of new architecture is that we combine distributed as well as parallel computing in such a way that it will be efficient to find out frequent item sets from large databases in less time. It also handle large database with efficiently than existing algorithms.

Frequent Pattern Mining and Current State of the Art

the association rules in large databases play a key role in data mining. The research is mainly aimed at considering prior researches, present working status and to restore the gaps between them with present known information. There are two problems regarding this context, they are identifying all frequent item sets and to generate constraints from them. Here, first problem, as it takes more processing time, is computationally costly. Consequently, many algorithms are proposed to solve this problem. Current study considers such algorithms and their related issues.

A Quantum Swarm Evolutionary Algorithm for mining association rules in large databases

Association rule mining aims to extract the correlation or causal structure existing between a set of frequent items or attributes in a database. These associations are represented by mean of rules. Association rule mining methods provide a robust but non-linear approach to find associations. The search for association rules is an NP-complete problem. The complexities mainly arise in exploiting huge number of database transactions and items. In this article we propose a new algorithm to extract the best rules in a reasonable time of execution but without assuring always the optimal solutions. The new derived algorithm is based on Quantum Swarm Evolutionary approach; it gives better results compared to genetic algorithms.

Mining Association Rules in Large Database by Implementing Pipelining Technique in Partition Algorithm

Mining for association rules between items in a large database of sales transactions has been described as an important database mining problem. In this paper we present an efficient algorithm for mining association rules that is faster than the previously proposed partition algorithms approximately m times where m is the number of stages in pipeline. The algorithm is also ideally suited for parallelization. General Terms Database, Data Mining, Algorithms

PPCI Algorithm for Mining Temporal Association Rules in Large Databases

In this paper an attempt has been made to develop a progressive partitioning and counting inference approach for mining association rules in temporal databases. A temporal database like a sales database is a set of transactions where each transaction T is a set of items in which each item contains an individual exhibition period. The existing models of association rule mining have problems in handling transactions due to a lack of consideration of the exhibition period of each individual item and lack of an equitable support counting basis for each item. As a remedy to this problem we propose an innovative algorithm PPCI that combines progressive partition approach with counting inference method to discover association rules in a temporal database. The basic idea of PPCI is to first segment the database into sub-databases in such a way that items in each sub-database will have either a common starting time or a common ending time. Then for each sub-database, PPCI progressively filters 1-itemset with a cumulative filtering threshold based on vital partitioning characteristics. Algorithm PPCI is also designed to employ a filtering threshold in each partition to prune out those cumulatively infrequent 1-itemsets early and it also uses counting inference approach to minimise as much as possible the number of pattern support counts performed when extracting frequent patterns. Explicitly the execution time of PPCI in order of magnitude is smaller than those required by the schemes which are directly extended from existing methods.

Incremental Mining Item Sets Based on H-mine and XML

Efficient discovery of association rules in large database is a well studied problem and several approaches have been proposed. However, the previously proposed methods still encounter some performance bottlenecks when mining databases is updated, such as inserted and deleted. In this paper, we propose an incremental updating technique based on H-mine and xml, for the maintenance of association rules when new transaction data is added to a transaction database. A performance evaluation shows that our algorithm is available and scalable.

Fast Algorithm for Mining Multi-Level Association Rules in Large Databases

Fast Algorithm for Mining Multi-Level Association Rules in Large Databases

Fuzzy versus quantitative association rules: a fair data-driven comparison

As opposed to quantitative association rule mining, fuzzy association rule mining is said to prevent the overestimation of boundary cases, as can be shown by small examples. Rule mining, however, becomes interesting in large databases, where the problem of boundary cases is less apparent and can be further suppressed by using sensible partitioning methods. A data-driven approach is used to investigate if there is a significant difference between quantitative and fuzzy association rules in large databases. The influence of the choice of a particular triangular norm in this respect is also examined.

Association rule descriptive language

Mining association rules in large databases is one of the most interesting data mining techniques in database communities. The number of association rules in a large database highly depends on rule’s support and confidence, and there might exist hundreds of rules in a very large database. The presentation of large number of rules in a very nice and noticeable way becomes highly challenging. In recent years researchers have developed several tools to visualize association rules. However, a large number of the tools cannot handle more than dozens of association rules. Furthermore, none of them can effectively manage association rules with multiple antecedents. Till now a uniform descriptive presentation technique has not been set up yet. We studied existing descriptive techniques in the context of visualization and introduced a graph-based technique as Unified Descriptive Language for Association Rules. The proposed technique can be used to extract the discovered rules of a very large database very conveniently and efficiently.

Efficient Graph-Based Algorithms for Discovering and Maintaining Association Rules in Large Databases

In this paper, we study the issues of mining and maintaining association rules in a large database of customer transactions. The problem of mining association rules can be mapped into the problems of finding large itemsets which are sets of items brought together in a sufficient number of transactions. We revise a graph-based algorithm to further speed up the process of itemset generation. In addition, we extend our revised algorithm to maintain discovered association rules when incremental or decremental updates are made to the databases. Experimental results show the efficiency of our algorithms. The revised algorithm is a significant improvement over the original one on mining association rules. The algorithms for maintaining association rules are more efficient than re-running the mining algorithms for the whole updated database and outperform previously proposed algorithms that need multiple passes over the database.

Mining multiple-level association rules in large databases

A top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the a priori principle. A group of variant algorithms is proposed based on the ways of sharing intermediate results, with the relative performance tested and analyzed. The enforcement of different interestingness measurements to find more interesting rules, and the relaxation of rule conditions for finding "level-crossing" association rules, are also investigated. The study shows that efficient algorithms can be developed from large databases for the discovery of interesting and strong multiple-level association rules.

On the Complexity of Mining Quantitative Association Rules

The discovery of quantitative association rules in large databases is considered an interesting and important research problem. Recently, different aspects of the problem have been studied, and several algorithms have been presented in the literature, among others in (Srikant and Agrawal, 1996; Fukuda et al., 1996a; Fukuda et al., 1996b; Yoda et al., 1997; Miller and Yang, 1997). An aspect of the problem that has so far been ignored, is its computational complexity. In this paper, we study the computational complexity of mining quantitative association rules.