Artificial Neural Networks: A Tutorial
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Approximation algorithms for geometric problems
Approximation algorithms for NP-hard problems
ACM Computing Surveys (CSUR)
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel clustering-based discriminant analysis
Pattern Recognition
Clustering and Embedding Using Commute Times
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering aggregation by probability accumulation
Pattern Recognition
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Information Retrieval
Structuralization of universes
Fuzzy Sets and Systems
An incremental nested partition method for data clustering
Pattern Recognition
Weighted partition consensus via kernels
Pattern Recognition
Image segmentation fusion using general ensemble clustering methods
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
A Bayesian approach for object classification based on clusters of SIFT local features
Expert Systems with Applications: An International Journal
Automated Construction of Classifications: Conceptual Clustering Versus Numerical Taxonomy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weighted association based methods for the combination of heterogeneous partitions
Pattern Recognition Letters
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The question of finding generic concepts and properties common to the different clustering approaches is a current problem. This inquire is addressed most thoroughly in Kleinberg's paper on the Impossibility Theorem (see [1]). Kleinberg introduced the notion of clustering function - a function that takes a dissimilarity measure defined on a data set S and returns a partition of S; and a set of simple properties for the study of such functions - Scale Invariance, Richness and Consistency. The main result of [1] is the Impossibility Theorem: there is no clustering method satisfying all these properties. This study has been accepted as a rigorous proof of the difficulty in finding a unified framework for different clustering approaches. Our goal in this paper is to provide primary concepts and results for the formal study of the various clustering approaches. To accomplish this, we discuss and expand on the ideas introduced by Kleinberg. Our guiding philosophy is to incorporate a crucial fact overlooked in the study conducted in [1] - clustering methods not only depend on the dissimilarity measure but also on other parameters such as dissimilarity thresholds, centroids, stop criteria, among others. This paper gives a formal definition of clustering method and reformulates the afore-mentioned properties, even it introduces some new. Contrary to the result obtained in [1], many of the methods discussed here satisfy all of our properties. With all these grounds in hand we glimpse a clue of unification among the different clustering approaches.