Estimating the Optimal Number of Clusters in Categorical Data Clustering by Silhouette Coefficient
- URL: http://arxiv.org/abs/2501.15542v1
- Date: Sun, 26 Jan 2025 14:29:11 GMT
- Title: Estimating the Optimal Number of Clusters in Categorical Data Clustering by Silhouette Coefficient
- Authors: Duy-Tai Dinh, Tsutomu Fujinami, Van-Nam Huynh,
- Abstract summary: This paper proposes an algorithm named k- SCC to estimate the optimal k in categorical data clustering.
Comparative experiments were conducted on both synthetic and real datasets to compare the performance of k- SCC.
- Score: 0.5939858158928473
- License:
- Abstract: The problem of estimating the number of clusters (say k) is one of the major challenges for the partitional clustering. This paper proposes an algorithm named k-SCC to estimate the optimal k in categorical data clustering. For the clustering step, the algorithm uses the kernel density estimation approach to define cluster centers. In addition, it uses an information-theoretic based dissimilarity to measure the distance between centers and objects in each cluster. The silhouette analysis based approach is then used to evaluate the quality of different clustering obtained in the former step to choose the best k. Comparative experiments were conducted on both synthetic and real datasets to compare the performance of k-SCC with three other algorithms. Experimental results show that k-SCC outperforms the compared algorithms in determining the number of clusters for each dataset.
Related papers
- Fast Clustering of Categorical Big Data [1.8416014644193066]
The K-Modes algorithm, developed for clustering categorical data, suffers from unreliable performances in clustering quality and clustering efficiency.
We investigate Bisecting K-Modes (BK-Modes), a successive bisecting process to find clusters, in examining how good the cluster centers out of the bisecting process will be.
Experimental results indicated good performances of BK-Modes both in the clustering quality and efficiency for large datasets.
arXiv Detail & Related papers (2025-02-10T22:19:08Z) - Self-Supervised Graph Embedding Clustering [70.36328717683297]
K-means one-step dimensionality reduction clustering method has made some progress in addressing the curse of dimensionality in clustering tasks.
We propose a unified framework that integrates manifold learning with K-means, resulting in the self-supervised graph embedding framework.
arXiv Detail & Related papers (2024-09-24T08:59:51Z) - Fuzzy K-Means Clustering without Cluster Centroids [21.256564324236333]
Fuzzy K-Means clustering is a critical technique in unsupervised data analysis.
This paper proposes a novel Fuzzy textitK-Means clustering algorithm that entirely eliminates the reliance on cluster centroids.
arXiv Detail & Related papers (2024-04-07T12:25:03Z) - Rethinking k-means from manifold learning perspective [122.38667613245151]
We present a new clustering algorithm which directly detects clusters of data without mean estimation.
Specifically, we construct distance matrix between data points by Butterworth filter.
To well exploit the complementary information embedded in different views, we leverage the tensor Schatten p-norm regularization.
arXiv Detail & Related papers (2023-05-12T03:01:41Z) - POCS-based Clustering Algorithm [0.0]
A novel clustering technique based on the projection onto convex set (POCS) method, called POCS-based clustering algorithm, is proposed in this paper.
The proposed POCS-based clustering algorithm exploits a parallel projection method of POCS to find appropriate cluster prototypes in the feature space.
The performance of the proposed POCS-based clustering algorithm is verified through experiments on various synthetic datasets.
arXiv Detail & Related papers (2022-08-15T12:33:09Z) - Gradient Based Clustering [72.15857783681658]
We propose a general approach for distance based clustering, using the gradient of the cost function that measures clustering quality.
The approach is an iterative two step procedure (alternating between cluster assignment and cluster center updates) and is applicable to a wide range of functions.
arXiv Detail & Related papers (2022-02-01T19:31:15Z) - A sampling-based approach for efficient clustering in large datasets [0.8952229340927184]
We propose a simple and efficient clustering method for high-dimensional data with a large number of clusters.
Our contribution is substantially more efficient than k-means as it does not require an all to all comparison of data points and clusters.
arXiv Detail & Related papers (2021-12-29T19:15:20Z) - Determinantal consensus clustering [77.34726150561087]
We propose the use of determinantal point processes or DPP for the random restart of clustering algorithms.
DPPs favor diversity of the center points within subsets.
We show through simulations that, contrary to DPP, this technique fails both to ensure diversity, and to obtain a good coverage of all data facets.
arXiv Detail & Related papers (2021-02-07T23:48:24Z) - A Multi-disciplinary Ensemble Algorithm for Clustering Heterogeneous
Datasets [0.76146285961466]
We propose a new evolutionary clustering algorithm (ECAStar) based on social class ranking and meta-heuristic algorithms.
ECAStar is integrated with recombinational evolutionary operators, Levy flight optimisation, and some statistical techniques.
Experiments are conducted to evaluate the ECAStar against five conventional approaches.
arXiv Detail & Related papers (2021-01-01T07:20:50Z) - Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
Existing scalable hierarchical clustering methods sacrifice quality for speed.
We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
arXiv Detail & Related papers (2020-10-22T15:58:35Z) - Clustering Binary Data by Application of Combinatorial Optimization
Heuristics [52.77024349608834]
We study clustering methods for binary data, first defining aggregation criteria that measure the compactness of clusters.
Five new and original methods are introduced, using neighborhoods and population behavior optimization metaheuristics.
From a set of 16 data tables generated by a quasi-Monte Carlo experiment, a comparison is performed for one of the aggregations using L1 dissimilarity, with hierarchical clustering, and a version of k-means: partitioning around medoids or PAM.
arXiv Detail & Related papers (2020-01-06T23:33:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.