Publications:
Nitin
Kumar, Dimitrios Gunopulos,
Vana Kalogeraki
Sensor
Network Coverage Restoration. In Proceedings of 2005 DCOSS International
conference on Distributed Computing in Sensor Systems, June 30- July 1,
2005, Marina del Ray, California, USA.
Nitin
Kumar , Nishanth Lolla
,
Eamonn Keogh ,
Stefano
Lonadi , Chotirat Ann
Ratanamahatana , Li Wie
.
Time-series Bitmaps: a practical visualization tool for working with
large time series databases. In Proceedings of 2005 SIAM International
conference on Data Mining, April 21-23, 2005, Newport Beach, CA, USA.
Banit
agarwal , Nitin Kumar
,
Mart Molle .
Controlling spams at the router level,
In
proceedings of 2005 IEEE International Conference on Communications, Next
Generation
Networks for Universal Services,
Seoul, South Korea, May 2005.
Li
Wei
, Nitin Kumar ,
Nishanth
Lolla , Eamonn Keogh
,
Stefano Lonadi ,
Chotirat
Ann Ratanamahatana . A
Practical Tool for Visualizing and Data Mining Medical Time Series.
In Proceedings of 18th IEEE International Symposium on Computer-Based
Medical Systems (CBMS), June 23-24, 2005, Trinity College, Dublin.
Li
Wei
, Nitin Kumar ,
Nishanth
Lolla , Eamonn Keogh
,
Stefano Lonadi ,
Chotirat
Ann Ratanamahatana .
Assumption-Free
Anomaly Detection in Time Series. In
Proceedings of 17th International Scientific and Statistical Database
Management Conference (SSDBM), June 27-29, 2005, UC Santa Barbara, CA, USA.
Maria Halkidi, Nitin Kumar, Dimitrios Gunopulos, Michalis Vazirgiannis, Carlotta Domeniconi A Framework for Semi-supervised Learning based on Subjective and Objective Clustering Criteria. In Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM) Novemver 27-30, 2005, New Orleans, Louisiana, USA.
Journals:
Maria Halkidi, Dimitrios Gunopulos, Michalis Vazirgiannis, Nitin Kumar, Carlotta Domeniconi . A Clustering Framework based on Subjective and Objective Validity Criteria , accepted in ACM Transactions on Knowledge Discovery from Data.
Technical Reports:
MS Thesis 2005:
A
Framework for Semi-Supervised Learning Based on Subjective and Objective Cluster
Validity Criteria.