NDLI: Data mining with Temporal Abstractions: learning rules from time series

Content Provider	Springer Nature Link
Author	Sacchi, Lucia Larizza, Cristiana Combi, Carlo Bellazzi, Riccardo
Copyright Year	2007
Abstract	A large volume of research in temporal data mining is focusing on discovering temporal rules from time-stamped data. The majority of the methods proposed so far have been mainly devoted to the mining of temporal rules which describe relationships between data sequences or instantaneous events and do not consider the presence of complex temporal patterns into the dataset. Such complex patterns, such as trends or up and down behaviors, are often very interesting for the users. In this paper we propose a new kind of temporal association rule and the related extraction algorithm; the learned rules involve complex temporal patterns in both their antecedent and consequent. Within our proposed approach, the user defines a set of complex patterns of interest that constitute the basis for the construction of the temporal rule; such complex patterns are represented and retrieved in the data through the formalism of knowledge-based Temporal Abstractions. An Apriori-like algorithm looks then for meaningful temporal relationships (in particular, precedence temporal relationships) among the complex patterns of interest. The paper presents the results obtained by the rule extraction algorithm on a simulated dataset and on two different datasets related to biomedical applications: the first one concerns the analysis of time series coming from the monitoring of different clinical variables during hemodialysis sessions, while the other one deals with the biological problem of inferring relationships between genes from DNA microarray data.
Starting Page	217
Ending Page	247
Page Count	31
File Format	PDF
ISSN	13845810
Journal	Data Mining and Knowledge Discovery
Volume Number	15
Issue Number	2
e-ISSN	1573756X
Language	English
Publisher	Springer US
Publisher Date	2007-06-25
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Temporal data mining Rule discovery Temporal abstractions Biomedical time series Information Storage and Retrieval Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences Statistics Artificial Intelligence (incl. Robotics) Computing Methodologies Data Mining and Knowledge Discovery
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Computer Science Applications

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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