Algorithms for clustering data
Algorithms for clustering data
An automatic and stable clustering algorithm
Pattern Recognition Letters
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Density-Based Multiscale Data Condensation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
DHC: A Density-Based Hierarchical Clustering Method for Time Series Gene Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
A needle in a haystack: local one-class optimization
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
Efficiently Mining Gene Expression Data via a Novel Parameterless Clustering Method
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Robust one-class clustering using hybrid global and local search
ICML '05 Proceedings of the 22nd international conference on Machine learning
Iterative shrinking method for clustering problems
Pattern Recognition
A k-mean clustering algorithm for mixed numeric and categorical data
Data & Knowledge Engineering
In search of deterministic methods for initializing K-means and Gaussian mixture clustering
Intelligent Data Analysis
Bregman bubble clustering: A robust framework for mining dense clusters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Computation of initial modes for K-modes clustering algorithm using evidence accumulation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Bioinformatics with soft computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The classical notion of clustering is to induce an equivalence class partition on a set of points, each class, being a homogeneous group, is called a cluster. Since it is an equivalence class partition, a point must belong to one and exactly one cluster. However in many applications, data distributions are such that only a subset of the points tends to flock under some distinct clusters while others go random. The present paper introduces an algorithm to find an optimal subset of points (ideally filtering out the random ones) with sufficient grouping tendency. It builds the neighborhood population around every point and picks up top k dense regions with possible reshuffling of points in post-processing. Performance of the algorithm is evaluated with applications onto real and simulated data. Comparative analysis on different quality indices with some other state-of-the-art algorithms establishes effectiveness of the approach.