Information Retrieval
A Maximum Variance Cluster Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovery of climate indices using clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm
Neural Processing Letters
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Exploratory Data Analysis with MATLAB (Computer Science and Data Analysis)
Exploratory Data Analysis with MATLAB (Computer Science and Data Analysis)
ACM Transactions on Knowledge Discovery from Data (TKDD)
ST-DBSCAN: An algorithm for clustering spatial-temporal data
Data & Knowledge Engineering
Robust path-based spectral clustering
Pattern Recognition
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Clustering Uncertain Data Via K-Medoids
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
KNN-kernel density-based clustering for high-dimensional multivariate data
Computational Statistics & Data Analysis
Data clustering: a user’s dilemma
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Mining spatial-temporal clusters from geo-databases
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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Clustering is a powerful exploratory technique for extracting the knowledge of given data. Several clustering techniques that have been proposed require predetermined number of clusters. However, the triangular kernel-nearest neighbor-based clustering (TKNN) has been proven able to determine the number and member of clusters automatically. TKNN provides good solutions for clustering non-spherical and high-dimensional data without prior knowledge of data labels. On the other hand, there is no definite measure to evaluate the accuracy of the clustering result. In order to evaluate the performance of the proposed TKNN clustering algorithm, we utilized various benchmark classification datasets. Thus, TKNN is proposed for discovering true clusters with arbitrary shape, size and density contained in the datasets. The experimental results on benched-mark datasets showed the effectiveness of our technique. Our proposed TKNN achieved more accurate clustering results and required less time processing compared with k-means, ILGC, DBSCAN and KFCM.