HBIC: A Biclustering Algorithm for Heterogeneous Datasets

• URL: http://arxiv.org/abs/2408.13217v1
• Date: Fri, 23 Aug 2024 16:48:10 GMT
• Title: HBIC: A Biclustering Algorithm for Heterogeneous Datasets
• Authors: Adán José-García, Julie Jacques, Clément Chauvet, Vincent Sobanski, Clarisse Dhaenens,
• Abstract summary: Biclustering is an unsupervised machine-learning approach aiming to cluster rows and columns simultaneously in a data matrix. We introduce a biclustering approach called HBIC, capable of discovering meaningful biclusters in complex heterogeneous data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Biclustering is an unsupervised machine-learning approach aiming to cluster rows and columns simultaneously in a data matrix. Several biclustering algorithms have been proposed for handling numeric datasets. However, real-world data mining problems often involve heterogeneous datasets with mixed attributes. To address this challenge, we introduce a biclustering approach called HBIC, capable of discovering meaningful biclusters in complex heterogeneous data, including numeric, binary, and categorical data. The approach comprises two stages: bicluster generation and bicluster model selection. In the initial stage, several candidate biclusters are generated iteratively by adding and removing rows and columns based on the frequency of values in the original matrix. In the second stage, we introduce two approaches for selecting the most suitable biclusters by considering their size and homogeneity. Through a series of experiments, we investigated the suitability of our approach on a synthetic benchmark and in a biomedical application involving clinical data of systemic sclerosis patients. The evaluation comparing our method to existing approaches demonstrates its ability to discover high-quality biclusters from heterogeneous data. Our biclustering approach is a starting point for heterogeneous bicluster discovery, leading to a better understanding of complex underlying data structures.

Related papers

• Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters [64.31109141089598]
  We introduce scUNC, an innovative multi-view clustering approach tailored for single-cell data. scUNC seamlessly integrates information from different views without the need for a predefined number of clusters. We conducted a comprehensive evaluation of scUNC using three distinct single-cell datasets.
  arXiv  Detail & Related papers  (2023-11-28T08:34:58Z)
• 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)
• Biclustering Algorithms Based on Metaheuristics: A Review [0.0]
  Biclustering is an unsupervised machine learning technique that simultaneously clusters rows and columns in a data matrix. Finding significant biclusters is an NP-hard problem that can be formulated as an optimization problem. Different metaheuristics have been applied to biclustering problems because of their exploratory capability of solving complex optimization problems in reasonable time.
  arXiv  Detail & Related papers  (2022-03-30T12:16:32Z)
• Clustering Ensemble Meets Low-rank Tensor Approximation [50.21581880045667]
  This paper explores the problem of clustering ensemble, which aims to combine multiple base clusterings to produce better performance than that of the individual one. We propose a novel low-rank tensor approximation-based method to solve the problem from a global perspective. Experimental results over 7 benchmark data sets show that the proposed model achieves a breakthrough in clustering performance, compared with 12 state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-12-16T13:01:37Z)
• 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)
• Biclustering with Alternating K-Means [5.089110111757978]
  We provide a new formulation of the biclustering problem based on the idea of minimizing the empirical clustering risk. We propose a simple and novel algorithm that finds a local minimum by alternating the use of an adapted version of the k-means clustering algorithm between columns and rows. The results demonstrate that our algorithm is able to detect meaningful structures in the data and outperform other competing biclustering methods in various settings and situations.
  arXiv  Detail & Related papers  (2020-09-09T20:15:24Z)
• A Novel Granular-Based Bi-Clustering Method of Deep Mining the Co-Expressed Genes [76.84066556597342]
  Bi-clustering methods are used to mine bi-clusters whose subsets of samples (genes) are co-regulated under their test conditions. Unfortunately, traditional bi-clustering methods are not fully effective in discovering such bi-clusters. We propose a novel bi-clustering method by involving here the theory of Granular Computing.
  arXiv  Detail & Related papers  (2020-05-12T02:04:40Z)
• Bi-objective Optimization of Biclustering with Binary Data [0.0]
  Clustering consists of partitioning data objects into subsets called clusters according to some similarity criteria. This paper addresses a quasi-clustering that allows overlapping of clusters, and which we link to biclustering. Biclustering simultaneously groups the objects and features so that a specific group of objects has a special group of features.
  arXiv  Detail & Related papers  (2020-02-09T21:49:26Z)
• 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.