New approach for the sequential pattern mining of high-dimensional sequence databases

Liu, Hongyan; Lin, Fangzhou; He, Jun; Cai, Yunjue

doi:10.1016/j.dss.2010.08.029

Cited by 11 publications

(4 citation statements)

References 4 publications

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“…Table 1 shows a multidimensional multi-sequence database, where the corresponding events of two sequential dimensions in one tuple closely occur together. For example, in tuple 1, event (a) and event (1,2) closely occur together, likewise event (b,c) and event (2,3), event (b,c,d) and event (6), event (e) and event (4,5). A multi-dimensional multi-sequence database(MMSDB) is of schema (RID,A 1 ,A 2 ,…,A m , S 1, S 2 ,…S n ), where RID is a primary key, (A 1 ,A 2 ,…,A m ) is a set of dimensions and (S 1 ,S 2 ,…S n ) is a set of sequential dimensions.…”

Section: Problem Definitionsmentioning

confidence: 99%

“…Lin presented METISP [5] to find time constraint sequential patterns rapidly in large databases by effective time-indexing and a pattern-growth strategy. H Liu et al came up with TD-Seq [6], which exploits a top-down transposition-based searching strategy as well as a new support counting method, for mining sequential patterns from high-dimensional sequence databases. T Hong et al put forward a novel incremental mining algorithm for maintaining sequential patterns, which is based on the concept of pre-large sequences to reduce the need for rescanning original databases [7].…”

Section: Introductionmentioning

confidence: 99%

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Exploring multi-dimensional sequential patterns across multi-dimensional multi-sequence databases

Huang¹,

Zuo²,

Bai³

et al. 2011

JDCTA

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Existing multi-dimensional sequential pattern mining methods only discover multi-dimensional sequential pattern in databases involving one sequential dimension. Since multi-dimensional sequential patterns may exist in databases containing more than one sequential dimension, in this paper, we present algorithm PSeq-MIDim for mining multi-dimensional sequential patterns from multiple sequential dimensions with multi-dimensional information, which makes up multi-dimensional multisequence databases. PSeq-MIDim applies PSeq to mine sequential patterns from multiple sequential dimensions, then forms the corresponding projected multi-dimensional database for each sequential pattern, and uses MIDim to mine multi-dimensional patterns within projected databases. PSeq performs sequential pattern mining from one sequential dimension, and then propagates the mined sequential patterns to other sequential dimension. Furthermore, the mined sequential patterns are represented as a lattice structure to provide guidelines for mining sequential patterns across multiple sequential dimensions. MIDim, which scans projected database only one time, makes the best of prefix-index technique for focused searching and finds multi-dimensional patterns rapidly. The experimental results show that PSeq-MIDim is efficient to find multi-dimensional sequential patterns from multi-dimensional multi-sequence databases.

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Section: Problem Definitionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring multi-dimensional sequential patterns across multi-dimensional multi-sequence databases

Huang¹,

Zuo²,

Bai³

et al. 2011

JDCTA

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show abstract

“…Pend and Liao [11] (2009) studied the problem of mining sequential patterns from multi-domain databases. Liu et al [12] (2010) also presented an algorithm-Top-Down mining of Sequential patterns (TD-Seq), for mining sequential patterns in high-dimensional stock sequence databases. Yiyong Xiao et al [13] (2011) proposed a new framework of mining association rules with time-windows on real-time transaction database.…”

Section: Introductionmentioning

confidence: 99%

Mining status-set Sequential Pattern based on frequent itemset for failure prediction in a temporal database with multiple status items monitored

Yuan

Xiao

Zhang

et al. 2014

The 26th Chinese Control and Decision Conference (2014 CCDC)

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In this study, we investigate the problem of status sequential pattern mining (SSPM) based on frequent status set for failure prediction. We present a general sequential pattern mining framework with new definitions (e.g., frequent status itemset) and redefinitions (e.g., Sequence, Sequential Pattern) on sequential patterns for the field of failure prediction with multiple status items monitored. Some new indexes such as coverage rate (CR), hold rate (HR), and factor set (FS) are introduced to discover interesting Strong SSP and related factor set of some important status itemsets. The Apriori-like algorithms are also developed particularly for SSPM with high computational efficiency, and numeric examples are provided to demonstrate the process of SSPM for failure prediction. It shows that the proposed algorithm for SSPM is effective, capable of discovering meaningful sequential patterns with user-interested coverage rate and hold rate.

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“…Such inter-sentential language patterns can provide more precise information to improve the performance of causality detection because they can capture the associations of multiple words within and between sentences. Therefore, this study develops a text mining framework by extending the classical association rule mining algorithm [ 24 - 28 ] such that it can mine inter-sentential language patterns by associating frequently co-occurred patterns across the sentence boundary. The discovered patterns are then incorporated into a probabilistic model to detect causality between sentences.…”

Section: Introductionmentioning

confidence: 99%

Detecting causality from online psychiatric texts using inter-sentential language patterns

Wu¹,

Yu²,

Chang³

2012

BMC Med Inform Decis Mak

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BackgroundOnline psychiatric texts are natural language texts expressing depressive problems, published by Internet users via community-based web services such as web forums, message boards and blogs. Understanding the cause-effect relations embedded in these psychiatric texts can provide insight into the authors’ problems, thus increasing the effectiveness of online psychiatric services.MethodsPrevious studies have proposed the use of word pairs extracted from a set of sentence pairs to identify cause-effect relations between sentences. A word pair is made up of two words, with one coming from the cause text span and the other from the effect text span. Analysis of the relationship between these words can be used to capture individual word associations between cause and effect sentences. For instance, (broke up, life) and (boyfriend, meaningless) are two word pairs extracted from the sentence pair: “I broke up with my boyfriend. Life is now meaningless to me”. The major limitation of word pairs is that individual words in sentences usually cannot reflect the exact meaning of the cause and effect events, and thus may produce semantically incomplete word pairs, as the previous examples show. Therefore, this study proposes the use of inter-sentential language patterns such as ≪broke up, boyfriend>,

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New approach for the sequential pattern mining of high-dimensional sequence databases

Cited by 11 publications

References 4 publications

Exploring multi-dimensional sequential patterns across multi-dimensional multi-sequence databases

Exploring multi-dimensional sequential patterns across multi-dimensional multi-sequence databases

Mining status-set Sequential Pattern based on frequent itemset for failure prediction in a temporal database with multiple status items monitored

Detecting causality from online psychiatric texts using inter-sentential language patterns

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