Local algorithms for interactive clustering

Pranjal Awasthi, Maria Balcan, Konstantin Voevodski
; Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):550-558, 2014.

Abstract

We study the design of interactive clustering algorithms for data sets satisfying natural stability assumptions. Our algorithms start with any initial clustering and only make local changes in each step; both are desirable features in many applications. We show that in this constrained setting one can still design provably efficient algorithms that produce accurate clusterings. We also show that our algorithms perform well on real-world data.

Cite this Paper


BibTeX
@InProceedings{pmlr-v32-awasthi14, title = {Local algorithms for interactive clustering}, author = {Pranjal Awasthi and Maria Balcan and Konstantin Voevodski}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {550--558}, year = {2014}, editor = {Eric P. Xing and Tony Jebara}, volume = {32}, number = {2}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/awasthi14.pdf}, url = {http://proceedings.mlr.press/v32/awasthi14.html}, abstract = {We study the design of interactive clustering algorithms for data sets satisfying natural stability assumptions. Our algorithms start with any initial clustering and only make local changes in each step; both are desirable features in many applications. We show that in this constrained setting one can still design provably efficient algorithms that produce accurate clusterings. We also show that our algorithms perform well on real-world data.} }
Endnote
%0 Conference Paper %T Local algorithms for interactive clustering %A Pranjal Awasthi %A Maria Balcan %A Konstantin Voevodski %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-awasthi14 %I PMLR %J Proceedings of Machine Learning Research %P 550--558 %U http://proceedings.mlr.press %V 32 %N 2 %W PMLR %X We study the design of interactive clustering algorithms for data sets satisfying natural stability assumptions. Our algorithms start with any initial clustering and only make local changes in each step; both are desirable features in many applications. We show that in this constrained setting one can still design provably efficient algorithms that produce accurate clusterings. We also show that our algorithms perform well on real-world data.
RIS
TY - CPAPER TI - Local algorithms for interactive clustering AU - Pranjal Awasthi AU - Maria Balcan AU - Konstantin Voevodski BT - Proceedings of the 31st International Conference on Machine Learning PY - 2014/01/27 DA - 2014/01/27 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-awasthi14 PB - PMLR SP - 550 DP - PMLR EP - 558 L1 - http://proceedings.mlr.press/v32/awasthi14.pdf UR - http://proceedings.mlr.press/v32/awasthi14.html AB - We study the design of interactive clustering algorithms for data sets satisfying natural stability assumptions. Our algorithms start with any initial clustering and only make local changes in each step; both are desirable features in many applications. We show that in this constrained setting one can still design provably efficient algorithms that produce accurate clusterings. We also show that our algorithms perform well on real-world data. ER -
APA
Awasthi, P., Balcan, M. & Voevodski, K.. (2014). Local algorithms for interactive clustering. Proceedings of the 31st International Conference on Machine Learning, in PMLR 32(2):550-558

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