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سند علمی

SQL Based Frequent Pattern Mining with FP-Growth

Xuequn Shang, Kai-Uwe Sattler, Ingolf Geist

چکیده

Scalable data mining in large databases is one of today’s real challenges to database research area. The integration of data mining with database systems is an essential component for any successful largescale data mining application. A fundamental component in data mining tasks is finding frequent patterns in a given dataset. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns. In this study we present an evaluation of SQL based frequent pattern mining with a novel frequent pattern growth (FP-growth) method, which is efficient and scalable for mining both long and short patterns without candidate generation. We examine some techniques to improve performance. In addition, we have made performance evaluation on DBMS with IBM DB2 UDB EEE V8.

اسناد مشابه

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    Data mining on large relational databases has gained popularity and its significance is well recognized. However, the performance of SQL based data mining is known to fall behind specialized implementation since the prohibitive nature of the cost associated with extracting knowle

  • Shaping SQL-Based Frequent Pattern Mining Algorithms

    سند علمی۲۰۰۵KDIDCsaba István Sidló, András Lukács

    Integration of data mining and database management systems could significantly ease the process of knowledge discovery in large databases. We consider implementations of frequent itemset mining algorithms, in particular pattern-growth algorithms similar to the top-down FP-growth

  • Pattern-growth Methods for Frequent Pattern Mining

    سند علمی۲۰۰۲Jian Pei, Ramesh Krishnamurti, Arthur L. Liestman

    Mining frequent patterns from large databases plays an essential role in many data mining tasks and has broad applications. Most of the previously proposed methods adopt apriorilike candidate-generation-and-test approaches. However, those methods may encounter serious challenges

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    سند علمی۲۰۱۰Hossein Tohidi, Hamidah Ibrahim

    An interesting method to frequent pattern mining without generating candidate pattern is called frequent-pattern growth, or simply FP-growth, which adopts a divide-and-conquer strategy as follows. First, it compresses the database representing frequent items into a frequent-patte

  • ZBDD-growth: An Efficient Method for Frequent Pattern Mining and Knowledge Indexing

    سند علمی۲۰۰۶Shin-ichi Minato, Hiroki Arimura

    (Abstract) Frequent pattern mining is one of the fundamental techniques for knowledge discovery and data mining. In the last decade, a number of efficient algorithms for frequent pattern mining have been presented, but most of them focused on just enumerating the patterns which s

  • Preference-Based Frequent Pattern Mining

    سند علمی۲۰۰۵IJDWMMoonjung Cho, Jian Pei, Haixun Wang, Wei Wang

    Frequent pattern mining is an important data mining problem with broad applications. Although there are many in-depth studies on efficient frequent pattern mining algorithms and constraint pushing techniques, the effectiveness of frequent pattern mining remains a serious concern: