TidFP: Mining Frequent Patterns in Different Databases with Transaction ID View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2009

AUTHORS

C. I. Ezeife , Dan Zhang

ABSTRACT

Since transaction identifiers (ids) are unique and would not usually be frequent, mining frequent patterns with transaction ids, showing records they occurred in, provides an efficient way to mine frequent patterns in many types of databases including multiple tabled and distributed databases. Existing work have not focused on mining frequent patterns with the transaction ids they occurred in. Many applications require finding strong associations between transaction id (e.g., certain drug) and the itemsets (e.g., certain adverse effects) to help deduce some pertinent lacking information (like how many people use this product in total) and information (like how many people have the adverse effects). This paper proposes a set of algorithms TidFPs, for mining frequent patterns with their transaction ids in a single transaction database, in a multiple tabled database, and in a distributed database. The proposed technique scans the database records only once even with level-wise Apriori-based mining techniques, stores frequent 1-items with their transaction id bitmap, outperforms traditional approaches and is extendible to other tree-based mining techniques as well as sequential mining. More... »

PAGES

125-137

References to SciGraph publications

  • 2001-01. SPADE: An Efficient Algorithm for Mining Frequent Sequences in MACHINE LEARNING
  • 1996. Mining sequential patterns: Generalizations and performance improvements in ADVANCES IN DATABASE TECHNOLOGY — EDBT '96
  • 2004-01. Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2005-01. Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2000. Mining Access Patterns Efficiently from Web Logs in KNOWLEDGE DISCOVERY AND DATA MINING. CURRENT ISSUES AND NEW APPLICATIONS
  • Book

    TITLE

    Data Warehousing and Knowledge Discovery

    ISBN

    978-3-642-03729-0
    978-3-642-03730-6

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-03730-6_11

    DOI

    http://dx.doi.org/10.1007/978-3-642-03730-6_11

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1032965112


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