Efficient periodicity mining of sequential patterns in a post-mining environment

Anwar, Fahad; Petrounias, Ilias; Kodogiannis, Vassilis; Tasseva, Violeta; Peneva, D.

doi:10.1109/is.2008.4670538

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…Lee et al (2008) introduced fuzzy periodicity to mine fuzzy periodic association rules. Anwar et al (2008) presented an efficient periodic patterns mining algorithm in postmining environment. Gu and Dong (2009) proposed an algorithm to find the local frequent periodic patterns in time series data.…”

Section: The Msapriori Algorithmmentioning

confidence: 99%

New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports

Chen

Huang

Lin

2011

Journal of Systems and Software

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Section: The Msapriori Algorithmmentioning

confidence: 99%

New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports

Chen

Huang

Lin

2011

Journal of Systems and Software

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“…This information can not only reduce the extra burden on the mining process by concentrating only on the userdefined segmented event database, but TP can also be used to observe the changes in the existing patterns by comparing the mining results based upon different time-periods. [7] <TP, SP SET , CL NB E >…”

Section: A Time Period (Tp)mentioning

confidence: 99%

“…{(1-5), (2,6), (3)(4)(5)(6)(7), (4-8), (5-9), (6-10)} 6 Table 1 Search windows for each sequential pattern…”

Section: Sp Search Windows Sw#mentioning

confidence: 99%

Discovery of events with negative behavior against given sequential patterns

Anwar

Petrounias

Morris

et al. 2010

2010 5th IEEE International Conference Intelligent Systems

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The dramatic drop in the prices of data collection and storage devices has not only enabled organisations to store almost every activity of their business processes, they can also retain every state of these activities as well. Availability of these masses of data also means that by implementing different data mining techniques we can yield more accurate and useful information to be used for important decision making. One of the key mining techniques on such data is to discover sequential patterns. Most of the existing sequential pattern mining approaches mainly deal with finding the positive behaviour of a sequential pattern that can help in predicting the next event after a sequence of events. In this paper we propose the concept of Negative Behaviour Against the Sequential Pattern (NBASP) that is to discover the events/event-sets which are unlikely to follow the given sequential pattern and discuss its applications in a variety of domains. A comprehensive problem definition and efficient algorithm to discover NBASP is presented.

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“…Elfeky et al [5] presented an approach to identify the periodicity of events from a given time sequence. Anwar et al [2] presented an algorithm based on periodicity mining of sequential patterns in a post-mining environment, in which periodicity mining is performed after finding the sequential patterns.…”

Section: Related Workmentioning

confidence: 99%

“…Previous research works in periodic patterns have concentrated on mining the repeated occurrence of events in time series or sequential data, and this is not directly applicable to the mining of periodic movement patterns. Anwar et al [2] recently introduced an interval validation process to mine the periodicity of patterns after all sequential patterns were obtained. However, their mining processes are inefficient in finding all sequential patterns and testing the periodicity of these patterns, as indicated in the experiment results (see Fig.…”

Section: Introductionmentioning

confidence: 99%

Mining periodic movement patterns of mobile phone users based on an efficient sampling approach

Wang

Cheng

2009

Appl Intell

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In m-commerce services, the periodic movement trends of customers at specific periods can be adopted to allocate the resources of telecommunications systems effectively and offer personalized location-based services. This study explores the mining of periodic maximal promising movement patterns. A detailed process for mining periodic maximal promising movement patterns based on graph mapping and sampling techniques is devised to enhance mining efficiency. First, a random sample of movement paths from time intervals is taken. Second, a unique path graph structure is built to store the movement paths obtained from the sample. Third, a graph traversal algorithm is developed to identify the maximal promising movement patterns. Finally, vector operations are undertaken to examine the maximal promising movement patterns in order to derive the periodic maximal promising movement patterns. Experimental results reveal that the sampling approach with mining has excellent execution efficiency and scalability in the investigation of periodic maximal promising movement patterns.

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Efficient periodicity mining of sequential patterns in a post-mining environment

Cited by 4 publications

References 6 publications

New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports

New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports

Discovery of events with negative behavior against given sequential patterns

Mining periodic movement patterns of mobile phone users based on an efficient sampling approach

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