A data mining approach to discovering reliable sequential patterns

Shyur, Huan‐Jyh; Jou, Chichang; Chang, Keng

doi:10.1016/j.jss.2013.03.105

Cited by 24 publications

(21 citation statements)

References 12 publications

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“…The existing SPM algorithms include three categories of Apriori based approaches, vertical format approaches and projection based approaches [8], and specific algorithms include: AprioriAll, AprioriSome, DynamicSome, GSP, MFS, PSP, SPADE, cSPADE, SPAM, LAPIN-SPAM, FreeSpan and PrefixSpan etc.…”

Section: Spmmentioning

confidence: 99%

“…Set the minimum support degree threshold value minsup=25%, the result of event SPM is shown as Table 3. 4 <CR> 58.6% ESL 5 <MR, BP> 27.4% ESL 6 <MR, TO> 55.3% ESL 7 <MR, CR> 42.4% ESL 8 <BP, TO> 27.4% ESL 9 <BP, CR> 27.4% ESL 10 <TO, CR> 27.4% ESL 11 <MR, (BP, TO)> 27.4% ESL 12 <MR, BP, CR> 27.4% ESL 13 <MR, TO, CR> 27.4% ESL 14 <(BP, TO), CR> 27.4% ESL 15 <MR, (BP, TO), CR> 27.4% Table 4 is the sequence-pattern support matrix SPS of cases and the result of similarity measure.…”

Section: Calculating Instancementioning

confidence: 99%

“…Problems such as customer buying behavior prediction, Web visiting mode prediction, disease diagnosis, network intrusion detection are all involved with sequential data and their mining [8]. It may be a good idea to take advantage of approaches in SPM to improve the capability of CBR for solving complex time sequence related problems [9].…”

Section: Introductionmentioning

confidence: 99%

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A Two- Step CBR Method Based on Sequential Data

Xiao¹

2016

IJDTA

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Section: Spmmentioning

confidence: 99%

Section: Calculating Instancementioning

confidence: 99%

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A Two- Step CBR Method Based on Sequential Data

Xiao¹

2016

IJDTA

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“…(Han et al, 2012). 이상치 탐색(Anomaly Detection)이 최근 중요하게 적용되고 있는 분야로는 <Table 1>과 같이 네트워크 침입 탐지, 금융사 기 탐지, 보건 및 의료 서비스 관련, 공공안전 및 보안 관련 등 에서 활용되고 있다 (Aggarwal, 2013 (Agrawal and Srikant, 1995;Pei et al, 2001;Shyur et al, 2013). Target Instance c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12 c13 c14 c15 c16 c17 c18 c19 c20 c1 0 3 4 4 0 3 3 3 0 3 4 4 0 3 3 4 0 2 4 5 c2 3 0 5 3 3 0 4 2 3 0 5 3 3 0 4 3 Container 008…”

Section: Earth Scienceunclassified

Detection of API(Anomaly Process Instance) Based on Distance for Process Mining

Jeon¹,

Bae²

2015

Journal of Korean Institute of Industrial Engineers

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There have been many attempts to find knowledge from data using conventional statistics, data mining, artificial intelligence, machine learning and pattern recognition. In those research areas, knowledge is approached in two ways. Firstly, researchers discover knowledge represented in general features for universal recognition, and secondly, they discover exceptional and distinctive features. In process mining, an instance is sequential information bounded by case ID, known as process instance. Here, an exceptional process instance can cause a problem in the analysis and discovery algorithm. Hence, in this paper we develop a method to detect the knowledge of exceptional and distinctive features when performing process mining. We propose a method for anomaly detection named Distance-based Anomaly Process Instance Detection (DAPID) which utilizes distance between process instances. DAPID contributes to a discovery of distinctive characteristic of process instance. For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. Additionally, we experiment on real data from a domestic port terminal to demonstrate our proposed methodology. †

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“…Yang et al [11] adopted an efficient encoding strategy to speed up the efficiency of processing period segments in an event sequence, and combined with the projection method to quickly find the partial periodic patterns in the recursive process. Shyur et al [9] proposed to discover frequent sequential patterns with probability of inter arrival time of consecutive items. They imposed minimum timeprobability constraint on sequential patterns, so that fewer but more reliable patterns will be obtained.…”

Section: Related Workmentioning

confidence: 99%

Timed sequential pattern mining based on confidence in accumulated intervals

Jou

Shyur

Yen

2014

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)

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Many applications of sequential patterns require a guarantee of a particular event happening within a period of time. We propose CAI-PrefixSpan, a new data mining algorithm to obtain confident timed sequential patterns from sequential databases. Based on PrefixSpan, it takes advantage of the pattern-growth approach. After a particular event sequence, it would first calculate the confidence level regarding the eventual occurrence of a particular event. For those pass the minimal confidence requirement, it then computes the minimal time interval that satisfies the support requirement. It then generates corresponding projected databases, and applies itself recursively on the projected databases. With the timing information, it obtains fewer but more confident sequential patterns. CAIPrefixSpan is implemented along with PrefixSpan. They are compared in terms of numbers of patterns obtained and execution efficiency. Our effectiveness and performance study shows that CAI-PrefixSpan is a valuable and efficient approach in obtaining timed sequential patterns.

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A data mining approach to discovering reliable sequential patterns

Cited by 24 publications

References 12 publications

A Two- Step CBR Method Based on Sequential Data

A Two- Step CBR Method Based on Sequential Data

Detection of API(Anomaly Process Instance) Based on Distance for Process Mining

Timed sequential pattern mining based on confidence in accumulated intervals

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