Sequence Mining in Categorical Domains: Algorithms and Applications

Zaki, Mohammed J.

doi:10.1007/3-540-44565-x_8

Cited by 6 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It makes only three database scans, one for 1-length sequential patterns, another for 2-length sequential patterns and one more for generating all other sequential patterns. An extended version of spade was proposed, denoted cspade, incorporating constraints like max-gap, max-span and length (Zaki, M. J., 2000). SPAM (Ayres, Flannick, Gehrke, & Yiu, 2002) generates sequential patterns using a depth-first search using effective pruning techniques.…”

Section: Related Workmentioning

confidence: 99%

A Constraint Programming Approach for Web Log Mining

Kemmar

Lebbah

Loudni

2016

International Journal of Information Technology and Web Engineering

View full text Add to dashboard Cite

Mining web access patterns consists in extracting knowledge from server log files. This problem is represented as a sequential pattern mining problem (SPM) which allows to extract patterns which are sequences of accesses that occur frequently in the web log file. There are in the literature many efficient algorithms to solve SMP (e.g., GSP, SPADE, PrefixSpan, WAP-tree, LAPIN, PLWAP). Despite the effectiveness of these methods, they do not allow to express and to handle new constraints defined on patterns, new implementations are required. Recently, many approaches based on constraint programming (CP) was proposed to solve SPM in a declarative and generic way. Since no CP-based approach was applied for mining web access patterns, the authors introduce in this paper an efficient CP-based approach for solving the web log mining problem. They bring back the problem of web log mining to SPM within a CP environment which enables to handle various constraints. Experimental results on non-trivial web log mining problems show the effectiveness of the authors' CP-based mining approach.

show abstract

Section: Related Workmentioning

confidence: 99%

A Constraint Programming Approach for Web Log Mining

Kemmar

Lebbah

Loudni

2016

International Journal of Information Technology and Web Engineering

View full text Add to dashboard Cite

show abstract

“…If the current event is a target event, the set of event types contained in the most recent time window become a new transaction in D. Finally, we use association-rule mining 31 to find large eventsets, that is, eventsets with frequency above minimum support (e.g., a priori algorithm). Our work is in some sense related to the area of sequential mining, [11][12][13][14] in which traditional association mining is extended to search for frequent subsequences.…”

Section: Target Events and Correlated Eventsmentioning

confidence: 99%

“…Recent years have seen an explosion in the study of data-mining techniques looking for different forms of temporal patterns. [11][12][13][14] A common technique is to find frequent subsequences of events in the data. An additional step, however, is needed to integrate these patterns into a model for prediction.…”

Section: Prediction Techniquesmentioning

confidence: 99%

Predictive algorithms in the management of computer systems

et al. 2002

View full text Add to dashboard Cite

Predictive algorithms play a crucial role in systems management by alerting the user to potential failures. We report on three case studies dealing with the prediction of failures in computer systems: (1) long-term prediction of performance variables (e.g., disk utilization), (2) short-term prediction of abnormal behavior (e.g., threshold violations), and (3) short-term prediction of system events (e.g., router failure). Empirical results show that predictive algorithms can be successfully employed in the estimation of performance variables and the prediction of critical events.

show abstract

“…For this problem, Pei et al [5], Srikant and Agrawal [6], and Zaki [7] proposed methods that efficiently discover frequent sequential patterns based on their support from discrete sequential data. Here, the support is a well-known criterion that evaluates their frequency.…”

Section: Introductionmentioning

confidence: 99%

“…Using the Apriori property, the method can avoid generating redundant candidate sequential patterns. Also, the method proposed by Zaki [7] introduced vertical ID lists. Each list is composed of both sequence number of sequential data and the time of occurrence of an item included in the sequence.…”

Section: Introductionmentioning

confidence: 99%

Discovery of Sequential Patterns Coinciding with Analysts’ Interests

Sakurai¹,

Kitahara²,

Orihara³

et al. 2008

JCP

View full text Add to dashboard Cite

This paper proposes a new sequential pattern mining method. The method introduces a new evaluation criterion satisfying the Apriori property. The criterion is calculated by the frequency of the sequential pattern and the minimum frequency of items included in the items. It extracts sequential patterns that can be rules predicting future items with high probability. Also, the method introduces new constraints. The constraints extract item sets composed of items whose attributes are different and extracts sequential patterns composed of item sets whose attribute sets are equal to one another. The proposed method efficiently discovers sequential patterns coinciding with analysts' interests by combining the criterion and the constraints. The paper verifies the effectiveness of the proposed method by applying it to medical examination data.

show abstract

Sequence Mining in Categorical Domains: Algorithms and Applications

Cited by 6 publications

References 8 publications

A Constraint Programming Approach for Web Log Mining

A Constraint Programming Approach for Web Log Mining

Predictive algorithms in the management of computer systems

Discovery of Sequential Patterns Coinciding with Analysts’ Interests

Contact Info

Product

Resources

About