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Title:
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MINING THE MOST K-FREQUENT ITEMSETS WITH TS-TREE |
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Author(s):
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Savo Tomović , Predrag Staniić |
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ISBN:
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978-972-8924-93-5 |
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Editors:
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Pedro Isaías, Bebo White and Miguel Baptista Nunes |
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Year:
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2009 |
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Edition:
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1 |
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Keywords:
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Top-k mining concept, frequent itemset mining, association analysis, FP-Growth algorithm |
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Type:
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Full Paper |
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First Page:
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606 |
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Last Page:
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613 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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In this paper we present TS-Growth algorithm that takes a pattern-growth approach (Han et al, 2000.) and Rymon's set
enumeration framework (Rymon, 1992.) for mining the most k-frequent itemsets. Top-k mining concept has been
proposed because it is difficult to predict how many frequent itemsets will be mined with a specified minimum support.
The Top-k mining concept is based on an algorithm for mining the number of most k frequent itemsets ordered according
to their support values. TS-Growth algorithm uses compact data structure called TS-tree (TS-tree will contain itemsets
from the input dataset with its support and because of that we called this tree a Total Support Tree or TS-tree) to store
candidate itemsets and extracts the most k-frequent itemsets directly from this structure. The algorithm requires just two
database scans. |
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