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PAMI
2006
134views more  PAMI 2006»
9 years 11 months ago
A Genetic Algorithm Using Hyper-Quadtrees for Low-Dimensional K-means Clustering
The k-means algorithm is widely used for clustering because of its computational efficiency. Given n points in d-dimensional space and the number of desired clusters k, k-means see...
Michael Laszlo, Sumitra Mukherjee
BMCBI
2008
142views more  BMCBI 2008»
9 years 12 months ago
Genetic weighted k-means algorithm for clustering large-scale gene expression data
Background: The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It...
Fang-Xiang Wu
NIPS
1994
10 years 1 months ago
Convergence Properties of the K-Means Algorithms
This paper studies the convergence properties of the well known K-Means clustering algorithm. The K-Means algorithm can be described either as a gradient descent algorithmor by sl...
Léon Bottou, Yoshua Bengio
CIMCA
2008
IEEE
10 years 1 months ago
A Clustering Algorithm Incorporating Density and Direction
This paper analyses the advantages and disadvantages of the K-means algorithm and the DENCLUE algorithm. In order to realise the automation of clustering analysis and eliminate hu...
Yu-Chen Song, Michael J. O'Grady, Gregory M. P. O'...
CSIE
2009
IEEE
10 years 4 months ago
K-Means on Commodity GPUs with CUDA
K-means algorithm is one of the most famous unsupervised clustering algorithms. Many theoretical improvements for the performance of original algorithms have been put forward, whi...
Hong-tao Bai, Li-li He, Dan-tong Ouyang, Zhan-shan...
ICDM
2002
IEEE
191views Data Mining» more  ICDM 2002»
10 years 4 months ago
Iterative Clustering of High Dimensional Text Data Augmented by Local Search
The k-means algorithm with cosine similarity, also known as the spherical k-means algorithm, is a popular method for clustering document collections. However, spherical k-means ca...
Inderjit S. Dhillon, Yuqiang Guan, J. Kogan
GECCO
2004
Springer
124views Optimization» more  GECCO 2004»
10 years 5 months ago
Clustering with Niching Genetic K-means Algorithm
GA-based clustering algorithms often employ either simple GA, steady state GA or their variants and fail to consistently and efficiently identify high quality solutions (best known...
Weiguo Sheng, Allan Tucker, Xiaohui Liu
ICALP
2005
Springer
10 years 5 months ago
Linear Time Algorithms for Clustering Problems in Any Dimensions
Abstract. We generalize the k-means algorithm presented by the authors [14] and show that the resulting algorithm can solve a larger class of clustering problems that satisfy certa...
Amit Kumar, Yogish Sabharwal, Sandeep Sen
ICDM
2006
IEEE
89views Data Mining» more  ICDM 2006»
10 years 5 months ago
On the Lower Bound of Local Optimums in K-Means Algorithm
The k-means algorithm is a popular clustering method used in many different fields of computer science, such as data mining, machine learning and information retrieval. However, ...
Zhenjie Zhang, Bing Tian Dai, Anthony K. H. Tung
ICDM
2007
IEEE
119views Data Mining» more  ICDM 2007»
10 years 6 months ago
Reducing UK-Means to K-Means
This paper proposes an optimisation to the UK-means algorithm, which generalises the k-means algorithm to handle objects whose locations are uncertain. The location of each object...
Sau Dan Lee, Ben Kao, Reynold Cheng
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