NDLI: Subjectively interesting alternative clusterings

Content Provider	Springer Nature Link
Author	Kontonasios, Kleanthis Nikolaos Bie, Tijl
Copyright Year	2013
Abstract	We deploy a recently proposed framework for mining subjectively interesting patterns from data to the problem of alternative clustering, where patterns are sets of clusters (clusterings) in the data. This framework outlines how subjective interestingness of patterns (here, clusterings) can be quantified using sound information theoretic concepts. We demonstrate how it motivates a new objective function quantifying the interestingness of a clustering, automatically accounting for a user’s prior beliefs and for redundancies between the discovered patterns.Directly searching for the optimal set of clusterings defined in this way is hard. However, the optimization problem can be solved approximately if clusterings are generated iteratively. In this iterative scheme, each subsequent clustering is maximally interesting given the whole set of previously generated clusterings, automatically trading off interestingness with non-redundancy. Although generating each clustering in an iterative fashion is computationally hard as well, we develop an approximation technique similar to spectral clustering algorithms.Our method can generate as many clusterings as the user requires. Subjective evaluation or the value of the objective function can guide the termination of the process. In addition our method allows varying the number of clusters in each successive clustering.Experiments on artificial and real-world datasets show that the mined clusterings fulfill the requirements of a good clustering solution by being both non-redundant and of high compactness. Comparison with existing solutions shows that our approach compares favourably with regard to well-known objective measures of similarity and quality of clusterings, even though it is not designed to directly optimize them.
Starting Page	31
Ending Page	56
Page Count	26
File Format	PDF
ISSN	08856125
Journal	Machine Learning
Volume Number	98
Issue Number	1-2
e-ISSN	15730565
Language	English
Publisher	Springer US
Publisher Date	2013-03-07
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Subjective interestingness Maximum entropy modelling Alternative clustering Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Computing Methodologies Simulation and Modeling Language Translation and Linguistics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

Sl.	Authority	Responsibilities	Communication Details
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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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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