10,020 research outputs found
How to find frequent patterns?
An improved version of DF, the depth-first implementation of Apriori, is presented.Given a database of (e.g., supermarket) transactions, the DF algorithm builds a so-called trie that contains all frequent itemsets, i.e., all itemsets that are contained in at least `minsup' transactions with `minsup' a given threshold value.In the trie, there is a one-to-one correspondence between the paths and the frequent itemsets.The new version, called DF+, differs from DF in that its data structure representing the database is borrowed from the FP-growth algorithm. So it combines the compact FP-growth data structure with the efficient trie-building method in DF.
Efficient Incremental Breadth-Depth XML Event Mining
Many applications log a large amount of events continuously. Extracting
interesting knowledge from logged events is an emerging active research area in
data mining. In this context, we propose an approach for mining frequent events
and association rules from logged events in XML format. This approach is
composed of two-main phases: I) constructing a novel tree structure called
Frequency XML-based Tree (FXT), which contains the frequency of events to be
mined; II) querying the constructed FXT using XQuery to discover frequent
itemsets and association rules. The FXT is constructed with a single-pass over
logged data. We implement the proposed algorithm and study various performance
issues. The performance study shows that the algorithm is efficient, for both
constructing the FXT and discovering association rules
HybridMiner: Mining Maximal Frequent Itemsets Using Hybrid Database Representation Approach
In this paper we present a novel hybrid (arraybased layout and vertical
bitmap layout) database representation approach for mining complete Maximal
Frequent Itemset (MFI) on sparse and large datasets. Our work is novel in terms
of scalability, item search order and two horizontal and vertical projection
techniques. We also present a maximal algorithm using this hybrid database
representation approach. Different experimental results on real and sparse
benchmark datasets show that our approach is better than previous state of art
maximal algorithms.Comment: 8 Pages In the proceedings of 9th IEEE-INMIC 2005, Karachi, Pakistan,
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A Tight Upper Bound on the Number of Candidate Patterns
In the context of mining for frequent patterns using the standard levelwise
algorithm, the following question arises: given the current level and the
current set of frequent patterns, what is the maximal number of candidate
patterns that can be generated on the next level? We answer this question by
providing a tight upper bound, derived from a combinatorial result from the
sixties by Kruskal and Katona. Our result is useful to reduce the number of
database scans
How to find frequent patterns?
An improved version of DF, the depth-first implementation of Apriori, is presented.
Given a database of (e.g., supermarket) transactions, the DF algorithm builds a so-called trie that contains all frequent itemsets, i.e., all itemsets that are contained in at least `minsup' transactions with `minsup' a given threshold value.
In the trie, there is a one-to-one correspondence between the paths and the frequent itemsets.
The new version, called DF+, differs from DF in that its data structure representing the database is borrowed from the FP-growth algorithm. So it combines the compact FP-growth data structure with the efficient trie-building method in DF
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