A self-adaptive and robust fission clustering algorithm via heat diffusion and maximal turning angle

• URL: http://arxiv.org/abs/2102.03794v1
• Date: Sun, 7 Feb 2021 13:16:47 GMT
• Title: A self-adaptive and robust fission clustering algorithm via heat diffusion and maximal turning angle
• Authors: Yu Han, Shizhan Lu, Haiyan Xu
• Abstract summary: A novel and fast clustering algorithm, fission clustering algorithm, is proposed in recent year. We propose a robust fission clustering (RFC) algorithm and a self-adaptive noise identification method.
• Score: 4.246818236277977
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cluster analysis, which focuses on the grouping and categorization of similar elements, is widely used in various fields of research. A novel and fast clustering algorithm, fission clustering algorithm, is proposed in recent year. In this article, we propose a robust fission clustering (RFC) algorithm and a self-adaptive noise identification method. The RFC and the self-adaptive noise identification method are combine to propose a self-adaptive robust fission clustering (SARFC) algorithm. Several frequently-used datasets were applied to test the performance of the proposed clustering approach and to compare the results with those of other algorithms. The comprehensive comparisons indicate that the proposed method has advantages over other common methods.

Related papers

• A Modular Spatial Clustering Algorithm with Noise Specification [0.0]
  Bacteria-Farm algorithm is inspired by the growth of bacteria in closed experimental farms. In contrast with other clustering algorithms, our algorithm also has a provision to specify the amount of noise to be excluded during clustering.
  arXiv  Detail & Related papers  (2023-09-18T18:05:06Z)
• 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)
• A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning [20.995946115633963]
  We propose a new parameter-free ART-based topological clustering algorithm capable of continual learning by introducing parameter estimation methods. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to the state-of-the-art clustering algorithms without any parameter pre-specifications.
  arXiv  Detail & Related papers  (2023-05-01T01:04:07Z)
• A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments [54.172993875654015]
  The paper proposes a family of communication efficient methods for distributed learning in heterogeneous environments. One-shot approach, based on local computations at the users and a clustering based aggregation step at the server is shown to provide strong learning guarantees. For strongly convex problems it is shown that, as long as the number of data points per user is above a threshold, the proposed approach achieves order-optimal mean-squared error rates in terms of the sample size.
  arXiv  Detail & Related papers  (2022-09-22T09:04:10Z)
• 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)
• Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning [8.581682204722894]
  This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from a distribution of data points. For the improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed.
  arXiv  Detail & Related papers  (2022-01-26T02:34:52Z)
• Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments [75.8001929811943]
  We introduce a realistic and challenging, multi-source and multi-room acoustic environment. We present an improved clustering control strategy that takes into account the variability of the acoustic scene. The proposed approach is optimized using clustering-based measures and validated via a network-wide classification task.
  arXiv  Detail & Related papers  (2021-06-07T14:51:39Z)
• Fuzzy clustering algorithms with distance metric learning and entropy regularization [0.0]
  This paper proposes fuzzy clustering algorithms based on Euclidean, City-block and Mahalanobis distances and entropy regularization. Several experiments on synthetic and real datasets, including its application to noisy image texture segmentation, demonstrate the usefulness of these adaptive clustering methods.
  arXiv  Detail & Related papers  (2021-02-18T18:19:04Z)
• 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)
• On the Efficiency of K-Means Clustering: Evaluation, Optimization, and Algorithm Selection [20.900296096958446]
  This paper presents a thorough evaluation of the existing methods that accelerate Lloyd's algorithm for fast k-means clustering. Within UniK, we thoroughly evaluate the pros and cons of existing methods using multiple performance metrics on a number of datasets. We derive an optimized algorithm over UniK, which effectively hybridizes multiple existing methods for more aggressive pruning.
  arXiv  Detail & Related papers  (2020-10-13T19:45:30Z)
• Differentially Private Clustering: Tight Approximation Ratios [57.89473217052714]
  We give efficient differentially private algorithms for basic clustering problems. Our results imply an improved algorithm for the Sample and Aggregate privacy framework. One of the tools used in our 1-Cluster algorithm can be employed to get a faster quantum algorithm for ClosestPair in a moderate number of dimensions.
  arXiv  Detail & Related papers  (2020-08-18T16:22:06Z)

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.