A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data

• URL: http://arxiv.org/abs/2407.03389v1
• Date: Wed, 3 Jul 2024 09:06:19 GMT
• Title: A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data
• Authors: Efthymios Costa, Ioanna Papatsouma, Angelos Markos,
• Abstract summary: We present an information-theoretic method for clustering mixed-type data, that is, data consisting of both continuous and categorical variables. The method is a variant of the Deterministic Information Bottleneck algorithm which optimally compresses the data while retaining relevant information about the underlying structure.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present an information-theoretic method for clustering mixed-type data, that is, data consisting of both continuous and categorical variables. The method is a variant of the Deterministic Information Bottleneck algorithm which optimally compresses the data while retaining relevant information about the underlying structure. We compare the performance of the proposed method to that of three well-established clustering methods (KAMILA, K-Prototypes, and Partitioning Around Medoids with Gower's dissimilarity) on simulated and real-world datasets. The results demonstrate that the proposed approach represents a competitive alternative to conventional clustering techniques under specific conditions.

Related papers

• Mixture of multilayer stochastic block models for multiview clustering [0.0]
  We propose an original method for aggregating multiple clustering coming from different sources of information. The identifiability of the model parameters is established and a variational Bayesian EM algorithm is proposed for the estimation of these parameters. The method is utilized to analyze global food trading networks, leading to structures of interest.
  arXiv  Detail & Related papers  (2024-01-09T17:15:47Z)
• Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework [74.25493157757943]
  We formulate a novel clustering model, which exploits the non-negative feature property and incorporates the multi-view information into a unified joint learning framework. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features.
  arXiv  Detail & Related papers  (2022-11-03T08:18:27Z)
• 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)
• Clustering Optimisation Method for Highly Connected Biological Data [0.0]
  We show how a simple metric for connectivity clustering evaluation leads to an optimised segmentation of biological data. The novelty of the work resides in the creation of a simple optimisation method for clustering crowded data.
  arXiv  Detail & Related papers  (2022-08-08T17:33:32Z)
• Data Clustering as an Emergent Consensus of Autonomous Agents [0.0]
  We present a data segmentation method based on a first-order density-induced consensus protocol. We provide a mathematically rigorous analysis of the consensus model leading to the stopping criteria of the data segmentation.
  arXiv  Detail & Related papers  (2022-04-22T09:11:35Z)
• A Proposition-Level Clustering Approach for Multi-Document Summarization [82.4616498914049]
  We revisit the clustering approach, grouping together propositions for more precise information alignment. Our method detects salient propositions, clusters them into paraphrastic clusters, and generates a representative sentence for each cluster by fusing its propositions. Our summarization method improves over the previous state-of-the-art MDS method in the DUC 2004 and TAC 2011 datasets.
  arXiv  Detail & Related papers  (2021-12-16T10:34:22Z)
• Deep Conditional Gaussian Mixture Model for Constrained Clustering [7.070883800886882]
  Constrained clustering can leverage prior information on a growing amount of only partially labeled data. We propose a novel framework for constrained clustering that is intuitive, interpretable, and can be trained efficiently in the framework of gradient variational inference.
  arXiv  Detail & Related papers  (2021-06-11T13:38:09Z)
• 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)
• Too Much Information Kills Information: A Clustering Perspective [6.375668163098171]
  We propose a simple, but novel approach for variance-based k-clustering tasks, including in which is the widely known k-means clustering. The proposed approach picks a sampling subset from the given dataset and makes decisions based on the data information in the subset only. With certain assumptions, the resulting clustering is provably good to estimate the optimum of the variance-based objective with high probability.
  arXiv  Detail & Related papers  (2020-09-16T01:54:26Z)
• Conjoined Dirichlet Process [63.89763375457853]
  We develop a novel, non-parametric probabilistic biclustering method based on Dirichlet processes to identify biclusters with strong co-occurrence in both rows and columns. We apply our method to two different applications, text mining and gene expression analysis, and demonstrate that our method improves bicluster extraction in many settings compared to existing approaches.
  arXiv  Detail & Related papers  (2020-02-08T19:41:23Z)
• 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.