Mining Sequential Alarm Patterns in a Telecommunication Database

Wu, Pei‐Hsin; Peng, Wen-Chih; Chen, Ming-Syan

doi:10.1007/3-540-45432-2_4

Cited by 13 publications

(13 citation statements)

References 10 publications

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“…Wu et al (2001) [4] specifies the referent urgent window d beforehand, such that, in a sequential alarm pattern, the interval between adjacent alarm events is within the time range For example, with set to 6 hour, the sequential alarm pattern (a, b, c) implies that a, b and c happen in that order and the interval between a and b, and between b and c is under 6 hour. Lastly, manila [5] specifies the window width (win) to find frequent episodes in sequences of events.…”

Section: A Previous Researchmentioning

confidence: 99%

Improved clustering technique for ITI-PrefixSpan

Bhatt

Dayma

2013

2013 Nirma University International Conference on Engineering (NUiCONE)

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Sequential data mining is the process to find out the frequent sub-sequences from the given sequential dataset. Sequential pattern mining can only reveal the sequence (order) of items, but it does not determined the time interval between two successive events. Time interval sequential mining is process to find out sequential patterns with time interval between two successive events. In this paper, we will introduce the new cluster technique so we will get dynamic cluster range rather than fixed. We improve the result of new ITI-PrefixSpan and compare our algorithm with other algorithms in terms of computing time and memory.

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Section: A Previous Researchmentioning

confidence: 99%

Improved clustering technique for ITI-PrefixSpan

Bhatt

Dayma

2013

2013 Nirma University International Conference on Engineering (NUiCONE)

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“…The discovery of sequential patterns problem was inspired by retailing industry problems. However, the results also apply to many scientific and business domains, such as stocks analysis, web log click streams analysis [2] [3], natural disasters (e.g., earthquakes), mining sequential alarm patterns in a telecommunication database [4], DNA sequences analysis and gene structures analysis [5], and so forth. Suppose the computer shop database contains list of customers, list of transaction time of each customer, and list of items bought for each customer's transaction time.…”

Section: Introductionmentioning

confidence: 98%

Sequential pattern mining using PrefixSpan with pseudoprojection and Separator Database

Saputra

Mean

2008

2008 International Symposium on Information Technology

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Sequential pattern mining is a new branch of data mining science that solves inter-transaction pattern mining problems. A comprehensive performance study has been reported that PrefixSpan, one of its algorithms, outperforms GSP, SPADE, as well as FreeSpan in most cases, and PrefixSpan integrated with pseudoprojection technique is the fastest among those tested algorithms. Nevertheless, Pseudoprojection technique, which requires maintaining and visiting the in-memory sequence database frequently until all patterns are found, consumes a considerable amount of memory and induces the algorithm to undertake redundant and unnecessary checks to this copy of original database into memory when the candidate patterns are examined. In this paper, we propose Separator Database to improve PrefixSpan with pseudoprojection through early removal of uneconomical in-memory sequence database. The experimental results show that Separator Database improves PrefixSpan with pseudoprojection. Future research includes exploring the use of Separator Database in PrefixSpan with pseudoprojection to improve mining constrained sequential patterns.

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“…An example of a sequential pattern is "A customer who bought a digital camera will buy an extra memory card later". This kind of data mining task is very useful in retail business to assist decision makers in making marketing strategies [4,9,12,15,23].…”

Section: Introductionmentioning

confidence: 99%

“…Clearly, the time-intervals between itemsets can offer useful information for retail business to provide the correct products to their customers at the right time. Therefore, some researches start to focus on discovering the sequential patterns with time-intervals between itemsets [2,6,8,11,13,17,18,19,23]. This kind of sequential pattern is called as time-interval sequential pattern.…”

Section: Introductionmentioning

confidence: 99%