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.