Scalable sequential pattern mining for biological sequences

Wang, Ke; Xu, Yabo; Yu, Jeffrey Xu

doi:10.1145/1031171.1031209

Cited by 86 publications

(47 citation statements)

References 14 publications

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“…SP-Index algorithm [9] finds FCP in two phases. The first phase is called as segment phase and the second phase is called as pattern phase.…”

Section: Various Methods For Frequent Contiguous Patterns 21 Sp-indementioning

confidence: 99%

Frequent Contiguous Pattern Mining Algorithms for Biological Data Sequences

Rajasekaran¹,

Arockiam²

2014

IJCA

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Transaction sequences in market-basket analysis have large set of alphabets with small length, whereas bio-sequences have small set of alphabets of long length with gap. There is the difference in pattern finding algorithms of these two sequences. The chances of repeatedly occurring small patterns are high in bio-sequences than in the transaction sequences. These repeatedly occurring small patterns are called as Frequent Contiguous Patterns (FCP). The challenging task in pattern finding of bio-sequences is to find FCP. FCP gives clues for genetic discovery, functional analysis and also helps to assemble a whole genome of species. Most of the existing FCP algorithms are all based on Apriori method. They require repeated scanning of the database and large number of intermediate tables to produce the results. So, these algorithms require large space and high computational time. In this paper, we are analyzing few of the currently available FCP algorithms with their advantages and disadvantages.

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“…SP-Index algorithm [9] finds FCP in two phases. The first phase is called as segment phase and the second phase is called as pattern phase.…”

Section: Various Methods For Frequent Contiguous Patterns 21 Sp-indementioning

confidence: 99%

Frequent Contiguous Pattern Mining Algorithms for Biological Data Sequences

Rajasekaran¹,

Arockiam²

2014

IJCA

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“…The sequential patterns mining problem was introduced by [13] in the context of the basket market problem and applied with success in many fields such as biology [29], Web mining [30,31] or the consumer marketing [32].…”

Section: Sequential Patterns Miningmentioning

confidence: 99%

A knowledge discovery process for spatiotemporal data: Application to river water quality monitoring

Alatrista-Salas

Azé

Bringay

et al. 2015

Ecological Informatics

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Rapid population growth, and human activities (such as agriculture, industry, transports,...) development have increased vulnerability risk for water resources. Due to the complexity of natural processes and the numerous interactions between hydro-systems and human pressures, water quality is difficult to be quantified. In this context, we present a knowledge discovery process applied to hydrological data. To achieve this objective, we combine successive methods to extract knowledge on data collected at stations located along several rivers. Firstly, data is pre-processed in order to obtain different spatial proximities. Later, we apply a standard algorithm to extract sequential patterns. Finally we propose a combination of two techniques (1) to filter patterns based on interest measure, and; (2) to group and present them graphically, to help the experts. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and river monitoring pressure data.

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“…For PrefixSpan, we used the pseudo-projection technique as suggested in Ref. 14), which makes PrefixSpan faster than SPAM 17) . The program for PrefixSpan was obtained directly from its authors' homepage (http://wwwsal.cs.uiuc.edu/hanj/pubs/software.htm).…”

Section: Experimental Environmentmentioning

confidence: 99%

Mining Sequential Patterns More Efficiently by Reducing the Cost of Scanning Sequence Databases

Wang

Asanuma

Kodama

et al. 2006

ipsjdc

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Sequential pattern mining is a useful technique used to discover frequent subsequences as patterns in a sequence database. Depending on the application, sequence databases vary by number of sequences, number of individual items, average length of sequences, and average length of potential patterns. In addition, to discover the necessary patterns in a sequence database, the support threshold may be set to different values. Thus, for a sequential pattern-mining algorithm, responsiveness should be achieved for all of these factors. For that purpose, we propose a candidate-driven pattern-growth sequential pattern-mining algorithm called FSPM (Fast Sequential Pattern Mining). A useful property of FSPM is that the sequential patterns concerning a user-specified item can be mined directly. Extensive experimental results show that, in most cases FSPM outperforms existing algorithms. An analytical performance study shows that it is the inherent potentiality of FSPM that makes it more effective.

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Scalable sequential pattern mining for biological sequences

Cited by 86 publications

References 14 publications

Frequent Contiguous Pattern Mining Algorithms for Biological Data Sequences

Frequent Contiguous Pattern Mining Algorithms for Biological Data Sequences

A knowledge discovery process for spatiotemporal data: Application to river water quality monitoring

Mining Sequential Patterns More Efficiently by Reducing the Cost of Scanning Sequence Databases

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