Search Results: 1 - 10 of 100 matches for " "
All listed articles are free for downloading (OA Articles)
Page 1 /100
Display every page Item
Frequent Pattern Mining for Multiple Minimum Supports with Support Tuning and Tree Maintenance on Incremental Database
F.A. Hoque,M. Debnath,N. Easmin,K. Rashed
Research Journal of Information Technology , 2011,
Abstract: Mining frequent patterns in transactional databases is an important part of the association rule mining. Frequent pattern mining algorithms with single minsup leads to rare item problem. Instead of setting single minsup for all items, we have used multiple minimum supports to discover frequent patterns. In this research, we have used multiple item support tree (MIS-Tree for short) to mine frequent patterns and proposed algorithms that provide (1) a complete facility of multiple support tuning (MS Tuning), and (2) maintenance of MIS-tree with incremental update of database. In a recent study on the same problem, MIS-tree and CFPgrowth algorithm has been developed to find all frequent item sets as well as to maintain MS tuning with some restrictions. In this study, we have modified the maintenance method by adding the benefit of flexible MS tuning without any restriction. Again, since database is subject to practice, an incremental updating technique has been proposed for maintenance of the MIS-tree after the database is updated. This maintenance ensures that every time an incremental database is added to the original database, the tree can be kept in correct status without costly rescanning of the aggregated database. Experiments on both synthetic and real data sets demonstrate the effectiveness of our proposed approaches.
Frequent Pattern Mining using CATSIM Tree  [PDF]
Ketan Modi,B.L.Pal
International Journal on Computer Science and Engineering , 2012,
Abstract: Efficient algorithms to discover frequent patterns are essential in data mining research. Frequent pattern mining is emerging as powerful tool for many business applications such as e-commerce, recommendersystems and supply chain management and group decision support systems to name a few. Several effective data structures, such as two-dimensional arrays, graphs, trees and tries have been proposed to collect candidate and frequent itemsets. It seems as the tree structure is most extractive to storing itemsets. The outstanding tree has been proposed so far is called FP-tree which is a prefix tree structure. Some advancement with the FP tree structure is proposed as CATS tree. CATS Tree extends the idea of FP-Tree to improve storage compression and allow frequent pattern mining without generation of candidate itemsets. It allows to mine only through asingle pass over the database. The efficiency of Apriori, FP-Growth, CATS Tree for incremental mining is very poor. In all of the above mentioned algorithms, it is required to generate tree repeatedly to support incremental mining. The implemented CATSIM Tree uses more memory compared to Apriori, FP-Growth and CATS Tree, but with advancement in technology, is not a major concern. In this work CATSIM Tree with modifications in CATS Tree is implemented to support incremental mining with better results.
Algorithm of mining frequent access paths from Web-Logs

CAO Zhong-sheng,TANG Shu-guang,YANG Liang-cong,

计算机应用 , 2006,
Abstract: The frequent access paths discovery is an important task of Web mining study. Focusing on how to discover the continuous frequent access paths form the Web-Logs, an algorithm named Ob-Mine was proposed. The Ob-Mine algorithm needed only one pass scanning over database. By creating the HBP-tree of Frequent Item, the Frequent Access Paths could be got. Exoeriments indicate that the OB-Mine algorithm is better than WAP algorithm.
Mining Frequent Subtrees from Dynamic Database

GUO Xin,DONG Jian-feng,ZHOU Qing-ping,

计算机科学 , 2011,
Abstract: On account of dynamic database's characteristic which is changing over time,a new algorithm aiming to mine frequent subtree from dynamic database was proposed. It put forward the support algorithm and subtree-searching space involving some concepts such as tree change probability, subtree expectation support and subtree dynamic support. The problem of mining frequent subtree from dynamic database was investigated. With the process of the subtrecsearching,algorithm definition pruning expressions and mix data structure could reduce subtre}searching space and improve frequcnt subtrec isomorphism speed efficiently. The experimental result showed that the new algorithm is effective and workable and has a better operating efficiency.
Research of multiple minimum supports frequent itemsets mining

ZHANG Hui-zhe,WANG Jian,

计算机应用 , 2007,
Abstract: Mining frequent itemsets algorithm based on multiple minimum supports was studied in this paper, because sometimes setting different minimum supports to mine frequent itemsets is necessary. A new Minimum Support tree (MS-tree) algorithm and a MS-growth algorithm to mine all frequent itemsets based on Frequent Pattern growth (FP-growth) were proposed. It solves the problem of MSapriori algorithm that it cannot generate association rules without scanning the database again. The experimental results show that the proposed algorithm is comparable to FP-growth algorithm, but the former can solve the problem of multiple minimum supports.
Frequent Patterns Mining and Incremental Updating Algorithm Based on Matrix

HE Hai-Tao,ZHANG Shi-Ling,

计算机科学 , 2008,
Abstract: Frequent patterns mining has been studied popularly in KDD research. However,little work has been done on incremental updating frequent patterns mining. A novel incremental updating pattern tree (INUP_Tree) structure is presented in this paper,which is constructed by scanning database only once. Besides,a new frequent pattern mining method (FPBM_Mine) based on conditional matrix and incremental updating algorithms INUPA are developed. The experiment result shows that the FPBM_Mine method is more efficient a...
Pattern discovering of Web user access pattern based on maximal frequent path method

WEI Chu-yuan,ZHANG Han-tao,

计算机应用 , 2007,
Abstract: As far as the issue on Web user access pattern is concerned, adopting Maximal Frequent Path (MFP) can mine more universal patterns. A new user access pattern tree named WUAP-tree was devised. Furthermore, a new algorithm named WUAP-mine was proposed for mining user access patterns, which was based on E-OEM model for page topological structure and users' browse path. The algorithm utilized WUAP-tree that could neither generate candidate sets nor use recursive ways. It could mine frequent Web users' access patterns by scanning transaction database and output-depth-first traversing WUAP-tree only once. The algorithm is very easy to query Web user access patterns from WUAP-tree directly. At last, theoretical analysis and experimental results prove its effectiveness and efficiency.
Novel Approach for Frequent Pattern Algorithm for Maximizing Frequent Patterns in Effective Time  [cached]
Rahul Sharma,Dr. Manish Manoria
International Journal of Computers & Technology , 2012,
Abstract: The essential aspect of mining association rules is to mine the frequent patterns. Due to native difficulty it is impossible to mine complete frequent patterns from a dense database. FP-growth algorithm has been implemented using an Array-based structure, known as the FP-tree,which is for storing compressed frequency information. Numerous experimental results have demonstrated that the algorithm performs extremely well. But in FP-growth algorithm, two traversals of FP-tree are needed for constructing the new conditional FP-tree. In this paper we present a novel Array Based Without Scanning Frequent Pattern (ABWSFP) tree technique that greatly reduces the need to traverse FP-trees, thus obtaining significantly improved performance for FP-tree based algorithms. The technique works especially well for large datasets. We then present a new algorithm which use the QFP-tree data structure in combination with the FP Tree- Experimental results show that the new algorithm outperform other algorithm in not only the speed of algorithms, but also their CPU consumption and their scalability.
FP-Tree Based Algorithms Analysis: FPGrowth, COFI-Tree and CT-PRO  [PDF]
Bharat Gupta,Dr. Deepak Garg
International Journal on Computer Science and Engineering , 2011,
Abstract: Mining frequent itemsets from the large transactional database is a very critical and important task. Many algorithms have been proposed from past many years, But FP-tree like algorithms are considered as very effective algorithms for efficiently mine frequent item sets. These algorithms considered as efficient because of their compact structure and also for less generation of candidates itemsets compare to Apriori and Apriori like algorithms. Therefore this paper aims to presents a basic Concepts of some of the algorithms (FP-Growth, COFI-Tree, CT-PRO) based upon the FP- Tree like structure for mining the frequent item sets along with their capabilities and comparisons.
Kuparala Chakrapani
Journal of Global Research in Computer Science , 2011,
Abstract: The essential aspect of mining association rules is to mine the frequent patterns. Due to intrinsic difficulty it is impossible to mine complete frequent patterns from a dense database. The quantity of mined patterns is generally large and it is firm to understand and utilize them. all frequent patterns are enclosed and compressed to maximal frequent patterns where the memory needed for storing them is smaller than that is required for storing complete patterns. Consequently, mining maximal frequent patterns provides a great value. This paper inorder to improve the structure of traditional FP-Tree presents an effective algorithm called IAFP-max for mining maximal frequent patterns based on improved FP-tree and array technique. The implementation of concept postfix sub- tree in the respective algorithm avoids generating the candidate of maximal frequent patterns in the mining process. Thus it reduces the memory consumed and also uses an array –based technique to the improved FP-Tree to reduce the traverse time. By the practical facts ,it represents that this algorithm overtakes many existing algorithms like MAFIA, Genax and FP max. Keywords: FP-Tree, IAFP, Frequent Patterns, itemsets, Array Technique.
Page 1 /100
Display every page Item

Copyright © 2008-2017 Open Access Library. All rights reserved.