Results 1 to 10 of about 315,009 (291)

Hierarchical word clustering - automatic thesaurus generation [PDF]

Neurocomputing, 2002
In this paper, we propose a hierarchical, lexical clustering neural network algorithm that automatically generates a thesaurus (synonym abstraction) using purely stochastic information derived from unstructured text corpora and requiring no prior word ...
Austin, J, Hodge, V J
core   +4 more sources

Cluster validity indices for automatic clustering: A comprehensive review [PDF]

Heliyon
The Cluster Validity Index is an integral part of clustering algorithms. It evaluates inter-cluster separation and intra-cluster cohesion of candidate clusters to determine the quality of potential solutions.
Abiodun M. Ikotun, Faustin Habyarimana, Absalom E. Ezugwu   +2 more
doaj   +2 more sources

Boosting k-means clustering with symbiotic organisms search for automatic clustering problems. [PDF]

PLoS ONE, 2022
Kmeans clustering algorithm is an iterative unsupervised learning algorithm that tries to partition the given dataset into k pre-defined distinct non-overlapping clusters where each data point belongs to only one group.
Abiodun M Ikotun, Absalom E Ezugwu
doaj   +2 more sources

Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing. [PDF]

PLoS ONE, 2015
This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, "MOPSOSA". The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets
Ahmad Abubaker, Adam Baharum, Mahmoud Alrefaei   +2 more
doaj   +2 more sources

Automatic Lifestate Identification and Clustering

International Journal of Population Data Science, 2023
Introduction & Background Summarising high-dimensional time series data across multiple entities is an increasingly prevalent problem because mass data collection has become routine in most domains.
Sam Smith, Gavin Smith, John Harvey
doaj   +2 more sources

Enhanced Automatic Span Segmentation of Airborne LiDAR Powerline Point Clouds: Mitigating Adjacent Powerline Interference [PDF]

Sensors
Extracting powerline point clouds from airborne LiDAR data and conducting 3D reconstruction has become a critical technical support for automatic transmission corridor inspection.
Yi Ma, Guofang Wang, Tianle Liu, Yifan Wang, Hao Geng, Wanshou Jiang   +5 more
doaj   +2 more sources

Benchmarking validity indices for evolutionary K-means clustering performance [PDF]

Scientific Reports
K-Means is a well-established clustering algorithm widely used in data analysis and various real-world applications. However, its requirement for a predefined number of clusters limits its effectiveness in automatic clustering tasks.
Abiodun M. Ikotun, Faustin Habyarimana, Absalom E. Ezugwu   +2 more
doaj   +2 more sources

Automatic sense clustering in EuroWordNet [PDF]

, 1998
This paper addresses ways in which we envisage to reduce the fine-grainedness of WordNet and express in a more systematic way the relations between its numerous sense distinctions.
Peters, I., Peters, W., Vossen, P.
core   +4 more sources

Automatic Clustering of DNA Sequences With Intelligent Techniques

IEEE Access, 2021
With the discovery of new DNAs, a fundamental problem arising is how to categorize those DNA sequences into correct species. Unfortunately, identifying all data groups correctly and assigning a set of DNAs into k clusters where k must be predefined are ...
Yasmin A. Badr, Khaled T. Wassif, Mahmoud Othman   +2 more
doaj   +1 more source

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

Applied Sciences, 2021
K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity.
Abiodun M. Ikotun, Mubarak S. Almutari, Absalom E. Ezugwu   +2 more
doaj   +1 more source