Influence of Swarm Intelligence in Data Clustering Mechanisms

• URL: http://arxiv.org/abs/2305.04217v1
• Date: Sun, 7 May 2023 08:40:50 GMT
• Title: Influence of Swarm Intelligence in Data Clustering Mechanisms
• Authors: Pitawelayalage Dasun Dileepa Pitawela, Gamage Upeksha Ganegoda
• Abstract summary: Nature inspired Swarm based algorithms are used for data clustering to cope with larger datasets with lack and inconsistency of data. This paper reviews the performances of these new approaches and compares which is best for certain problematic situation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data mining focuses on discovering interesting, non-trivial and meaningful information from large datasets. Data clustering is one of the unsupervised and descriptive data mining task which group data based on similarity features and physically stored together. As a partitioning clustering method, K-means is widely used due to its simplicity and easiness of implementation. But this method has limitations such as local optimal convergence and initial point sensibility. Due to these impediments, nature inspired Swarm based algorithms such as Artificial Bee Colony Algorithm, Ant Colony Optimization, Firefly Algorithm, Bat Algorithm and etc. are used for data clustering to cope with larger datasets with lack and inconsistency of data. In some cases, those algorithms are used with traditional approaches such as K-means as hybrid approaches to produce better results. This paper reviews the performances of these new approaches and compares which is best for certain problematic situation.

Related papers

• GBCT: An Efficient and Adaptive Granular-Ball Clustering Algorithm for Complex Data [49.56145012222276]
  We propose a new clustering algorithm called granular-ball clustering (GBCT) via granular-ball computing. GBCT forms clusters according to the relationship between granular-balls, instead of the traditional point relationship. As granular-balls can fit various complex data, GBCT performs much better in non-spherical data sets than other traditional clustering methods.
  arXiv  Detail & Related papers  (2024-10-17T07:32:05Z)
• Linear time Evidence Accumulation Clustering with KMeans [0.0]
  This work describes a trick which mimic the behavior of average linkage clustering. We found a way of computing efficiently the density of a partitioning, reducing the cost from a quadratic to linear complexity. The k-means results are comparable to the best state of the art in terms of NMI while keeping the computational cost low.
  arXiv  Detail & Related papers  (2023-11-15T14:12:59Z)
• Improved Distribution Matching for Dataset Condensation [91.55972945798531]
  We propose a novel dataset condensation method based on distribution matching. Our simple yet effective method outperforms most previous optimization-oriented methods with much fewer computational resources.
  arXiv  Detail & Related papers  (2023-07-19T04:07:33Z)
• 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)
• Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls [67.33923111887933]
  In this paper, the data is fuzzy iterated using granular-balls, and the membership degree of data only considers the two granular-balls where it is located. The formed fuzzy granular-balls set can use more processing methods in the face of different data scenarios.
  arXiv  Detail & Related papers  (2023-03-07T01:52:55Z)
• How to Use K-means for Big Data Clustering? [2.1165011830664677]
  K-means is the simplest and most widely used algorithm under the Euclidean Minimum Sum-of-Squares Clustering (MSSC) model. We propose a new parallel scheme of using K-means and K-means++ algorithms for big data clustering.
  arXiv  Detail & Related papers  (2022-04-14T08:18:01Z)
• Clustering Plotted Data by Image Segmentation [12.443102864446223]
  Clustering algorithms are one of the main analytical methods to detect patterns in unlabeled data. In this paper, we present a wholly different way of clustering points in 2-dimensional space, inspired by how humans cluster data. Our approach, Visual Clustering, has several advantages over traditional clustering algorithms.
  arXiv  Detail & Related papers  (2021-10-06T06:19:30Z)
• Robust Trimmed k-means [70.88503833248159]
  We propose Robust Trimmed k-means (RTKM) that simultaneously identifies outliers and clusters points. We show RTKM performs competitively with other methods on single membership data with outliers and multi-membership data without outliers.
  arXiv  Detail & Related papers  (2021-08-16T15:49:40Z)
• Clustering of Big Data with Mixed Features [3.3504365823045044]
  We develop a new clustering algorithm for large data of mixed type. The algorithm is capable of detecting outliers and clusters of relatively lower density values. We present experimental results to verify that our algorithm works well in practice.
  arXiv  Detail & Related papers  (2020-11-11T19:54:38Z)
• 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)
• FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data [59.50904660420082]
  Federated Learning (FL) has become a popular paradigm for learning from distributed data. To effectively utilize data at different devices without moving them to the cloud, algorithms such as the Federated Averaging (FedAvg) have adopted a "computation then aggregation" (CTA) model.
  arXiv  Detail & Related papers  (2020-05-22T23:07:42Z)

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.