SbFP-Growth: A Step to Remove the Bottleneck of FP-Tree View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019

AUTHORS

Shafiul Alom Ahmed , Bhabesh Nath , Abhijeet Talukdar

ABSTRACT

Many association rule mining techniques have been proposed and most of them are greatly dependent on physical memory. Therefore, the limited amount of physical memory becomes a bottleneck of the existing techniques for large-scale datasets. In this paper, we propose a new approach, (Secondary Storage-Based Frequent Pattern) SbFP-Growth, which is a modification to the FP-Growth algorithm. SbFP-Growth uses secondary storage to store the tree, and therefore, it overcomes the main memory bottleneck at FP-Growth algorithm. By this way, we are able to mine for frequent itemsets from databases of arbitrary size large datasets. Moreover, SbFP-Growth constructs complete tree (SbFP-Tree), i.e., a tree with minimum support count equal to one. It provides the freedom of mining rules for different lower minimum support values without reconstructing the tree from scratch. We can store the SbFP-Tree in secondary storage so that it can be mined later with any minimum support value. More... »

PAGES

285-296

References to SciGraph publications

  • 2008. Alternative Method for Incrementally Constructing the FP-Tree in INTELLIGENT TECHNIQUES AND TOOLS FOR NOVEL SYSTEM ARCHITECTURES
  • 2010-09. Mining top-K frequent itemsets through progressive sampling in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2007-08. Frequent pattern mining: current status and future directions in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2006-05. Sequential Pattern Mining in Multi-Databases via Multiple Alignment in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2005-11. GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2004-11. Efficient Mining of Frequent Patterns Using Ascending Frequency Ordered Prefix-Tree in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2005-03. Efficient Algorithms for Mining and Incremental Update of Maximal Frequent Sequences in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2004-01. Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2002-10. Discretization: An Enabling Technique in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2009-08. A novel hash-based approach for mining frequent itemsets over data streams requiring less memory space in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2009-04. DRFP-tree: disk-resident frequent pattern tree in APPLIED INTELLIGENCE
  • Book

    TITLE

    Recent Developments in Machine Learning and Data Analytics

    ISBN

    978-981-13-1279-3
    978-981-13-1280-9

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-981-13-1280-9_27

    DOI

    http://dx.doi.org/10.1007/978-981-13-1280-9_27

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1106929884


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