Relational interpretations of neighborhood operators and rough set approximation operators
Information Sciences—Informatics and Computer Science: An International Journal
ACM Computing Surveys (CSUR)
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
Rough Set-Based Clustering with Refinement Using Shannon's Entropy Theory
Computers & Mathematics with Applications
MMR: An algorithm for clustering categorical data using Rough Set Theory
Data & Knowledge Engineering
Expert Systems with Applications: An International Journal
A new measure of uncertainty based on knowledge granulation for rough sets
Information Sciences: an International Journal
Some issues about outlier detection in rough set theory
Expert Systems with Applications: An International Journal
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
IEEE Transactions on Pattern Analysis and Machine Intelligence
An experimental comparison of several clustering and initialization methods
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A data labeling method for clustering categorical data
Expert Systems with Applications: An International Journal
A framework for clustering categorical time-evolving data
IEEE Transactions on Fuzzy Systems
Weight selection in W-K-means algorithm with an application in color image segmentation
Computers & Mathematics with Applications
A comparative study of efficient initialization methods for the k-means clustering algorithm
Expert Systems with Applications: An International Journal
Computer Vision and Image Understanding
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As a simple clustering method, the traditional K-Means algorithm has been widely discussed and applied in pattern recognition and machine learning. However, the K-Means algorithm could not guarantee unique clustering result because initial cluster centers are chosen randomly. In this paper, the cohesion degree of the neighborhood of an object and the coupling degree between neighborhoods of objects are defined based on the neighborhood-based rough set model. Furthermore, a new initialization method is proposed, and the corresponding time complexity is analyzed as well. We study the influence of the three norms on clustering, and compare the clustering results of the K-means with the three different initialization methods. The experimental results illustrate the effectiveness of the proposed method.