Computational geometry: an introduction
Computational geometry: an introduction
Filtering search: a new approach to query answering
SIAM Journal on Computing
Algorithms for clustering data
Algorithms for clustering data
Symbolic clustering using a new dissimilarity measure
Pattern Recognition
A conceptual version of the K-means algorithm
Pattern Recognition Letters
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Indexing large metric spaces for similarity search queries
ACM Transactions on Database Systems (TODS)
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Principal Direction Divisive Partitioning
Data Mining and Knowledge Discovery
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
The new k-windows algorithm for improving the k-means clustering algorithm
Journal of Complexity
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
D-Index: Distance Searching Index for Metric Data Sets
Multimedia Tools and Applications
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
A Computational Geometry Approach to Web Personalization
CEC '04 Proceedings of the IEEE International Conference on E-Commerce Technology
Indexing High-Dimensional Data for Efficient In-Memory Similarity Search
IEEE Transactions on Knowledge and Data Engineering
Unsupervised clustering on dynamic databases
Pattern Recognition Letters
The ND-tree: a dynamic indexing technique for multidimensional non-ordered discrete data spaces
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Automated Construction of Classifications: Conceptual Clustering Versus Numerical Taxonomy
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Signal Processing
Enhancing principal direction divisive clustering
Pattern Recognition
| Hi-index | 0.98 |
Clustering methods are one of the key steps that lead to the transformation of data to knowledge. Clustering algorithms aim at partitioning an initial set of objects into disjoint groups (clusters) such that that objects in the same subset are more similar to each other than objects in different groups. In this paper we present a generalization of the k-Windows clustering algorithm in metric spaces. The original algorithm was designed to work on data with numerical values. The proposed generalization does not assume anything about the nature of the data per se, but only considers the definition of a distance function over the dataset. The efficiency of the proposed approach is demonstrated in various datasets.