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A new approach for discovering top-k sequential patterns based on the variety of items

Sakurai S., Nishizawa M.

Journal of Artificial Intelligence and Soft Computing Research

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2015

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Vol. 5, No. 2

141--153

EN

This paper proposes a method that discovers various sequential patterns from sequential data. The sequential data is a set of sequences. Each sequence is a row of item sets. Many previous methods discover frequent sequential patterns from the data. However, the patterns tend to be similar to each other because they are composed of limited items. The patterns do not always correspond to the interests of analysts. Therefore, this paper tackles on the issue discovering various sequential patterns. The proposed method decides redundant sequential patterns by evaluating the variety of items and deletes them based on three kinds of delete processes. It can discover various sequential patterns within the upper bound for the number of sequential patterns given by the analysts. This paper applies the method to the synthetic sequential data which is characterized by number of items, their kind, and length of sequence. The effect of the method is verified through numerical experiments.

2

Mining Therapeutic Patterns from Clinical Data for Juvenile Diabetes

Froelich W., Deja R., Deja G.

Fundamenta Informaticae

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2013

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Vol. 127, nr 1-4

513--528

EN

The disease of diabetes mellitus has spread in recent years across the world, and has thus become an even more important medical problem. Despite numerous solutions already proposed, the problem of management of glucose concentration in the blood of a diabetic patient still remains as a challenge and raises interest among researchers. The data-driven models of glucose-insulin interaction are one of the recent directions of research. In particular, a data-driven model can be constructed using the idea of sequential patterns as the knowledge representation method. In this paper a new hierarchical, template-based approach for mining sequential patterns is proposed. The paper proposes also to use functional abstractions for the representation and mining of clinical data. Due to the experts knowledge involved in the construction of functional abstractions and sequential templates, the discovered underlying template-based patters can be easily interpreted by physicians and are able to provide recommendations of medical therapy. The proposed methodology was validated by experiments using real clinical data of juvenile diabetes.

3

Mining Pharmacy Database Using Evolutionary Genetic Algorithm

Ykhlef M., ElGibreen H.

International Journal of Electronics and Telecommunications

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2010

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Vol. 56, No. 4

427-432

EN

Medication management is an important process in pharmacy field. Prescribing errors occur upstream in the process, and their effects can be perpetuated in subsequent steps. Prescription errors are an important issue for which conflicts with another prescribed medicine could cause severe harm for a patient. In addition, due to the shortage of pharmacists and to contain the cost of healthcare delivery, time is also an important issue. Former knowledge of prescriptions can reduce the errors, and discovery of such knowledge requires data mining techniques, such as Sequential Pattern. Moreover, Evolutionary Algorithms, such as Genetic Algorithm (GA), can find good rules in short time, thus it can be used to discover the Sequential Patterns in Pharmacy Database. In this paper GA is used to assess patient prescriptions based on former knowledge of series of prescriptions in order to extract sequenced patterns and predict unusual activities to reduce errors in timely manner.

4

Pruning discovered sequential patterns using minimum improvement threshold

Prinke S., Wojciechowski M., Zakrzewicz M.

Foundations of Computing and Decision Sciences

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2006

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Vol. 31, No. 1

43-57

EN

Discovery of sequential patterns is an important data mining problem with numerous applications. Sequential patterns are subsequences frequently occurring in a database of sequences of sets of items. In a basic scenario, the goal of sequential pattern mining is discovery of all patterns whose frequency exceeds a user-specified frequency threshold. The problem with such an approach is a huge number of sequential patterns which are likely to be returned for reasonable frequency thresholds. One possible solution to this problem is excluding the patterns which do not provide significantly more information than some other patterns in the result set. Two approaches falling into that category have been studied in the context of sequential patterns: discovery of maximal patterns and closed patterns. Unfortunately, the set of maximal patterns may not contain many important patterns with high frequency, and discovery of closed patterns may not reduce the number of resulting patterns for sparse datasets. Therefore, in this paper we propose and experimentally evaluate the minimum improvement criterion to be used in the post-processing phase to reduce the number of sequential patterns returned to the user. Our method is an adaptation of one of the methods previously proposed for association rules.