OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A Maximum Variance Cluster Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Compression, Clustering, and Pattern Discovery in Very High-Dimensional Discrete-Attribute Data Sets
IEEE Transactions on Knowledge and Data Engineering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Random projection trees and low dimensional manifolds
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Data Clustering: User's Dilemma
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
DENCLUE 2.0: fast clustering based on kernel density estimation
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Scalable and Memory-Efficient Clustering of Large-Scale Social Networks
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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Clustering offers significant insights in data analysis. Density based algorithms have emerged as flexible and efficient techniques, able to discover high-quality and potentially irregularly shaped- clusters. We present two fast density-based clustering algorithms based on random projections. Both algorithms demonstrate one to two orders of magnitude speedup compared to equivalent state-of-art density based techniques, even for modest-size datasets. We give a comprehensive analysis of both our algorithms and show runtime of O(dNlog2 N), for a d-dimensional dataset. Our first algorithm can be viewed as a fast variant of the OPTICS density-based algorithm, but using a softer definition of density combined with sampling. The second algorithm is parameter-less, and identifies areas separating clusters.