NDLI: Stream Mining Using Statistical Relational Learning

Content Provider	IEEE Xplore Digital Library
Author	Chandra, Swarup Sahs, Justin Khan, Latifur Thuraisingham, Bhavani Aggarwal, Charu
Copyright Year	2014
Abstract	Stream mining has gained popularity in recent years due to the availability of numerous data streams from sources such as social media and sensor networks. Data mining on such continuous streams possess a variety of challenges including concept drift and unbounded stream length. Traditional data mining approaches to these problems have difficulty incorporating relational domain knowledge and feature relationships, which can be used to improve the accuracy of a classifier. In this work, we model large data streams using statistical relational learning techniques for classification, in particular, we use a Markov Logic Network to capture relational features in structured data and show that this approach performs better for supervised learning than current state-of-the-art approaches. Additionally, we evaluate our approach with semi-supervised learning scenarios, where class labels are only partially available during training.
Starting Page	743
Ending Page	748
File Size	195712
Page Count	6
File Format	PDF
ISSN	15504786
e-ISBN	9781479943029
DOI	10.1109/ICDM.2014.144
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Accuracy Training Data models Grounding Markov random fields Classification Stream Mining Statistical Relational Learning
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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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