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SPoID: Incomplete Sequence Mining for Sequential Patterns

Abstract : Industrial databases often contain a large amount of unfilled information. During the knowledge discovery process one processing step is often necessary in order to remove these incomplete data either by deleting or assessing them. When the data mining task consists in mining for frequent sequences, incomplete data are, most of the time, deleted, which leads to an important loss of information. Extracted knowledge then becomes less representative of the database. Therefore we propose a method that uses the partial information contained in incomplete records, only temporary ignoring the missing part of the record. Experiments run on various synthetic datasets show the validity of our proposal as well in terms of quality as in terms of the robustness to the rate of missing values.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00136070
Contributor : Celine Fiot <>
Submitted on : Monday, March 12, 2007 - 10:38:01 AM
Last modification on : Thursday, May 24, 2018 - 3:59:20 PM

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  • HAL Id : lirmm-00136070, version 1

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Céline Fiot. SPoID: Incomplete Sequence Mining for Sequential Patterns. RR-07006, 2007. ⟨lirmm-00136070⟩

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