Efficient Association Rule Mining in Heterogeneous Data Base
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Abstract
Data mining techniques are used to discover hidden information from horizontal and vertical databases. Association rule discovery has emerged as an important problem in knowledge discovery and information mining. The affiliation mining errand comprises of distinguishing the continuous thing sets, and afterward shaping contingent ramifications rules among them. An efficient algorithm for the discovery of frequent item sets which forms the compute intensive phase of the task. A proficient calculation for the revelation of regular thing sets which structures the figure serious period of the assignment. Expanding interest for registering worldwide affiliation rules for the vertical databases has a place with distinctive locales in a manner that private information is not uncovered and site holder knows the worldwide discoveries and their individual information just. The coordination of even and vertical databases need to defeat the trouble of computational expense. To attain to this we propose a calculation for parallel and successive parceling to deliver a powerful aftereffect of better computational time, throughput, computational cost and bigger thing size in disseminated flat and vertical databases.
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TJPRC, 2013
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REDUCTION OF NUMBER OF ASSOCIATION RULES WITH INTER ITEMSET DISTANCE IN TRANSACTION DATABASES
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Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures - SPAA '97, 1997
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Implementation of Algorithms Related to Association Rules in Large Databases
IAEME PUBLICATION, 2013
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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.
A New Approach for Optimizing the Extraction of Association Rules
Engineering, Technology & Applied Science Research, 2023
Association rule methods are among the most used approaches for Knowledge Discovery in Databases (KDD), as they allow discovering and extracting hidden meaningful relationships between attributes or items in large datasets in the form of rules. Algorithms to extract these rules require considerable time and large memory spaces. This paper presents an algorithm that decomposes this complex problem into subproblems and processes items by category according to their support. Very frequent items and fairly frequent items are studied together. To evaluate the performance of the proposed algorithm, it was compared with Eclat and LCMFreq on two actual transactional databases. The experimental results showed that the proposed algorithm was faster in execution time and demonstrated its efficiency in memory consumption.
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International Journal of Computer Theory and Engineering, 2012
In this paper the problem of discovering association rules among items in extremely large databases has been considered. A novel mining algorithm named Improved Cluster Based Association Rules (ICBAR) has been proposed which can explore efficiently the large itemsets. Achieving this and initializing the cluster table (where transaction records with length k are placed in kth cluster table), database will be once scanned. Simultaneously an array with appropriate size for each itemset (named itemset array (IA)) will be created. Here kth element in the array of each itemset indicates number of transaction records in kth cluster table which have that itemset. Presented method not only prunes considerable amounts of data by comparing with the partial cluster tables but also reduces the number of large candidate itemset that must be checked in each cluster through itemset arrays. Performance and efficiency of proposed method has been compared with CBAR and Apriori algorithms. Experiments illustrate that ICBAR will do better than both of them.
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The knowledge discovery algorithms have become ineffective at the abundance of data and the need for fast algorithms or optimizing methods is required. To address this limitation, the objective of this work is to adapt a new method for optimizing the time of association rules extractions from large databases. Indeed, given a relational database (one relation) represented as a set of tuples, also called set of attributes, we transform the original database as a binary table (Bitmap table) containing binary numbers. Then, we use this Bitmap table to construct a data structure called Peano Tree stored as a binary file on which we apply a new algorithm called BF-ARM (extension of the well known Apriori algorithm). Since the database is loaded into a binary file, our proposed algorithm will traverse this file, and the processes of association rules extractions will be based on the file stored on disk. The BF-ARM algorithm is implemented and compared with Apriori, Apriori+ and RS-Rules+ a...
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Association Rule Mining (ARM) is one of the well know and most researched technique of data mining. There are so many ARM algorithms have been designed that their counting is a large number. In this paper we have surveyed the various ARM algorithms in four computing environments. The considered computing environments are sequential computing, parallel and distributed computing, grid computing and cloud computing. With the emergence of new computing paradigm, ARM algorithms have been designed by many researchers to improve the efficiency by utilizing the new paradigm. This paper represents the journey of ARM algorithms started from sequential algorithms, and through parallel and distributed, and grid based algorithms to the current state-of-the-art, along with the motives for adopting new machinery.
Scalable algorithms for association mining
IEEE Transactions on Knowledge and Data Engineering, 2000
AbstractÐAssociation rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent itemsets and then, forming conditional implication rules among them. In this paper, we present efficient algorithms for the discovery of frequent itemsets which forms the compute intensive phase of the task. The algorithms utilize the structural properties of frequent itemsets to facilitate fast discovery. The items are organized into a subset lattice search space, which is decomposed into small independent chunks or sublattices, which can be solved in memory. Efficient lattice traversal techniques are presented which quickly identify all the long frequent itemsets and their subsets if required. We also present the effect of using different database layout schemes combined with the proposed decomposition and traversal techniques. We experimentally compare the new algorithms against the previous approaches, obtaining improvements of more than an order of magnitude for our test databases.
Review on: Taut Extraction of Association Rule from Distributed Database
— There are many techniques to extract association rules from large datasets, but sometimes these datasets are distributed horizontally which is called strew database. In the strew database there are several sites or players that hold homogeneous database this database shares the same schema but hold information on different entities. For extracting association rules from such database the existing system is not so secure and efficient. The proposed system given here provides a secure and efficient solution for the problem stated above. Here we are going to use Fast Distributed mining (FDM) which is an unsecured distributed version of the Apriori algorithm. The main ingredients of the proposed system are two novel secure multi-party algorithms—one that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset held by another. This protocol offers enhanced privacy with respect to the protocol in [18]. In addition, it is simpler and is significantly more efficient in terms of communication rounds, communication cost and computational cost.
Efficient data-structures and parallel algorithms for association rules discovery
Proceedings of the Fifth Mexican International Conference in Computer Science, 2004. ENC 2004., 2004
Discovering patterns or frequent episodes in transactions is an important problem in data-mining for the purpose of infering deductive rules from them. Because of the huge size of the data to deal with, parallel algorithms have been designed for reducing both the execution time and the number of repeated passes over the database in order to reduce, as much as possible, I/O overheads. In this paper, we introduce new approaches for the implementation of two basic algorithms for association rules discovery (namely Apriori and Eclat). Our approaches combine efficient data structures to code different key information (line indexes, candidates) and we exhibit how to introduce parallelism for processing such data-structures.
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