Incremental Refinement of Mining Queries

Baralis, Elena; Psaila, Giuseppe

doi:10.1007/3-540-48298-9_19

Cited by 30 publications

(27 citation statements)

References 3 publications

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“…In [4] three relationships which occur between two association rule queries were identified. The relationships represented cases when results on one query can be used to efficiently answer the other.…”

Section: Related Workmentioning

confidence: 99%

A Study on Answering a Data Mining Query Using a Materialized View

Zakrzewicz

Morzy

Wojciechowski

2004

Lecture Notes in Computer Science

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Abstract.One of the classic data mining problems is discovery of frequent itemsets. This problem particularly attracts database community as it resembles traditional database querying. In this paper we consider a data mining system which supports storing of previous query results in the form of materialized data mining views. While numerous works have shown that reusing results of previous frequent itemset queries can significantly improve performance of data mining query processing, a thorough study of possible differences between the current query and a materialized view has not been presented yet. In this paper we classify possible differences into six classes, provide I/O cost analysis for all the classes, and experimentally evaluate the most promising ones.

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Section: Related Workmentioning

confidence: 99%

A Study on Answering a Data Mining Query Using a Materialized View

Zakrzewicz

Morzy

Wojciechowski

2004

Lecture Notes in Computer Science

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“…Optimizing sequences of data mining queries by caching techniques has been studied for sequences [19] or association rules [9,2,15,8]. However none of these works makes use of condensed representations and the problem of the size of the stored information is not studied.…”

Section: Introductionmentioning

confidence: 99%

Using Condensed Representations for Interactive Association Rule Mining

Jeudy

Boulicaut

2002

Principles of Data Mining and Knowledge Discovery

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Abstract. Association rule mining is a popular data mining task. It has an interactive and iterative nature, i.e., the user has to refine his mining queries until he is satisfied with the discovered patterns. To support such an interactive process, we propose to optimize sequences of queries by means of a cache that stores information from previous queries. Unlike related works, we use condensed representations like free and closed itemsets for both data mining and caching. This results in a much more efficient mining technique in highly correlated data and a much smaller cache than in previous approaches.

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“…For these two methods, Figure 9 shows the number of tree nodes 4 for different levels of overlapping and support thresholds of 1% and 2% respectively. The values for Common Building are sums of the number of nodes for both queries 5 . The experiments show that Common FP-tree requires significantly less memory than Common Building, and as expected memory savings increase with the level of overlapping.…”

Section: Resultsmentioning

confidence: 99%

“…Methods falling into that category that have been studied in the context of frequent itemset discovery are: incremental mining [7], caching intermediate query results [17], and reusing materialized complete [5][15] [16] or condensed [13] results of previous queries provided that syntactic differences between the queries satisfy certain conditions.…”

Section: Related Workmentioning

confidence: 99%

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Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth

Wojciechowski

Galecki

Gawronek

Lecture Notes in Computer Science

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Abstract. Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. Recently, a new problem of optimizing processing of sets of frequent itemset queries has been considered and two multiple query optimization techniques for frequent itemset queries: Mine Merge and Common Counting have been proposed and tested on the Apriori algorithm. In this paper we discuss and experimentally evaluate three strategies for concurrent processing of frequent itemset queries using FP-growth as a basic frequent itemset mining algorithm. The first strategy is Mine Merge, which does not depend on a particular mining algorithm and can be applied to FP-growth without modifications. The second is an implementation of the general idea of Common Counting for FP-growth. The last is a completely new strategy, motivated by identified shortcomings of the previous two strategies in the context of FP-growth.

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Incremental Refinement of Mining Queries

Cited by 30 publications

References 3 publications

A Study on Answering a Data Mining Query Using a Materialized View

A Study on Answering a Data Mining Query Using a Materialized View

Using Condensed Representations for Interactive Association Rule Mining

Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth

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