Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
P3C: A Robust Projected Clustering Algorithm
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A Unified View of Matrix Factorization Models
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Mining Text Data
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The problem of projected clustering was first proposed in the ACMSIGMOD Conference in 1999, and the Probabilistic Latent Semantic Indexing (PLSI) technique was independently proposed in the ACMSIGIR Conference in the same year. Since then, more than two thousand papers have been written on these problems by the database, data mining and information retrieval communities, along completely independent lines of work. In this paper, we show that these two problems are essentially equivalent, under a probabilistic interpretation to the projected clustering problem. We will show that the EM-algorithm, when applied to the probabilistic version of the projected clustering problem, can be almost identically interpreted as the PLSI technique. The implications of this equivalence are significant, in that they imply the cross-usability of many of the techniques which have been developed for these problems over the last decade. We hope that our observations about the equivalence of these problems will stimulate further research which can significantly improve the currently available solutions for either of these problems.