Results 1 to 10 of about 17,769,086 (225)
K-Means Cloning: Adaptive Spherical K-Means Clustering [PDF]
Algorithms, 2018 We propose a novel method for adaptive K-means clustering. The proposed method overcomes the problems of the traditional K-means algorithm. Specifically, the proposed method does not require prior knowledge of the number of clusters.
Abdel-Rahman Hedar +3 more
doaj +3 more sources
Optimized Cartesian K-Means [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2015 Product quantization-based approaches are effective to encode high-dimensional data points for approximate nearest neighbor search. The space is decomposed into a Cartesian product of low-dimensional subspaces, each of which generates a sub codebook. Data points are encoded as compact binary codes using these sub codebooks, and the distance between two
Wang, Jianfeng +5 more
openaire +4 more sources
Unsupervised K-Means Clustering Algorithm
IEEE Access, 2020 The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the
Kristina P. Sinaga, Miin-Shen Yang
doaj +2 more sources
The impact of neglecting feature scaling in k-means clustering. [PDF]
PLoS OneDespite the popularity of k-means clustering, feature scaling before applying it can be an essential yet often neglected step. In this study, feature scaling via five methods: Z-score, Min-Max normalization, Percentile transformation, Maximum absolute ...
Wongoutong C.
europepmc +2 more sources
The global k-means clustering algorithm
Pattern Recognition, 2003 Aristidis Likas, Nikos Vlassis
exaly +2 more sources
Barekeng, 2021 Mengembangkan wilayah untuk mengurangi kesenjangan dan menjamin pemerataan merupakan salah satu dari tujuh agenda Pembangunana RPJMN IV Tahun 2020-2024. Setiap wilayah tentunya memiliki potensi yang berbeda, baik potensi fisik maupun non-fisik. Perbedaan
Muhamad Budiman Johra
doaj +1 more source
Exact Acceleration of K-Means++ and K-Means|| [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 K-Means++ and its distributed variant K-Means|| have become de facto tools for selecting the initial seeds of K-means. While alternatives have been developed, the effectiveness, ease of implementation,and theoretical grounding of the K-means++ and || methods have made them difficult to "best" from a holistic perspective.
openaire +2 more sources
t-k-means: A ROBUST AND STABLE k-means VARIANT [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 $k$-means algorithm is one of the most classical clustering methods, which has been widely and successfully used in signal processing. However, due to the thin-tailed property of the Gaussian distribution, $k$-means algorithm suffers from relatively poor performance on the dataset containing heavy-tailed data or outliers.
Li, Yiming +5 more
openaire +2 more sources
Genetic K-means algorithm [PDF]
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1999 In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GA's used earlier in clustering employ either an expensive crossover operator to generate valid child chromosomes from parent chromosomes or a costly fitness function or both.
Krishna, K, Murty, Narasimha M
openaire +3 more sources
Stop using the elbow criterion for k-means and how to choose the number of clusters instead [PDF]
SIGKDD Explorations, 2022 A major challenge when using k-means clustering often is how to choose the parameter k, the number of clusters. In this letter, we want to point out that it is very easy to draw poor conclusions from a common heuristic, the "elbow method".
Erich Schubert
semanticscholar +1 more source