Neighborhood search based improved bat algorithm for data clustering

2022 ◽  
Author(s):  
Arvinder Kaur ◽  
Yugal Kumar
Keyword(s):  
Data Clustering ◽  
Bat Algorithm ◽  
Neighborhood Search

scholarly journals A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 786
Author(s):  
Yenny Villuendas-Rey ◽  
Eley Barroso-Cubas ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Swarm Intelligence ◽  
Data Clustering ◽  
Incomplete Data ◽  
Missing Values ◽  
Clustering Algorithms ◽  
Bat Algorithm ◽  
Hybrid Features ◽  
Bee Colony ◽  
Learning Tasks ◽  
Clustering Data

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.

scholarly journals Optimal mathematical programming and variable neighborhood search for k-modes categorical data clustering

2019 ◽  
Vol 90 ◽  
pp. 183-195 ◽  
Author(s):  
Yiyong Xiao ◽  
Changhao Huang ◽  
Jiaoying Huang ◽  
Ikou Kaku ◽  
Yuchun Xu
Keyword(s):  
Mathematical Programming ◽  
Categorical Data ◽  
Data Clustering ◽  
Variable Neighborhood Search ◽  
Neighborhood Search ◽  
Categorical Data Clustering

Hybrid enhanced shuffled bat algorithm for data clustering

2020 ◽  
Vol 17 (3/4) ◽  
pp. 323
Author(s):  
Hema Banati ◽  
Reshu Chaudhary
Keyword(s):  
Data Clustering ◽  
Bat Algorithm

Hybrid enhanced shuffled bat algorithm for data clustering

2020 ◽  
Vol 17 (3/4) ◽  
pp. 323
Author(s):  
Reshu Chaudhary ◽  
Hema Banati
Keyword(s):  
Data Clustering ◽  
Bat Algorithm

scholarly journals Clustering Tools for Integration of Satellite Remote Sensing Imagery and Proximal Soil Sensing Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1036 ◽  
Author(s):  
Md Saifuzzaman ◽  
Viacheslav Adamchuk ◽  
Roberto Buelvas ◽  
Asim Biswas ◽  
Shiv Prasher ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Data Clustering ◽  
Bare Soil ◽  
Neighborhood Search ◽  
Soil Variability ◽  
Agricultural Fields ◽  
Essential Information ◽  
Spatial Continuity ◽  
Proximal Soil Sensing

Remote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected by these sensors may provide essential information for precision or site-specific management in a production field. Data clustering techniques are crucial for data mining, and high-density data analysis is important for field management. A new clustering technique was introduced and compared with existing clustering tools to determine the relatively homogeneous parts of agricultural fields. A DUALEM-21S sensor, along with high-accuracy topography data, was used to characterize soil variability in three agricultural fields situated in Ontario, Canada. Sentinel-2 data assisted in quantifying bare soil and vegetation indices (VIs). The custom Neighborhood Search Analyst (NSA) data clustering tool was implemented using Python scripts. In this algorithm, part of the variance of each data layer is accounted for by subdividing the field into smaller, relatively homogeneous, areas. The algorithm’s attributes were illustrated using field elevation, shallow and deep apparent electrical conductivity (ECa), and several VIs. The unique feature of this proposed protocol was the successful development of user-friendly and open source options for defining the spatial continuity of each group and for use in the zone delineation process.

scholarly journals MBA-LF: A NEW DATA CLUSTERING METHOD USING MODIFIED BAT ALGORITHM AND LEVY FLIGHT

2015 ◽  
Vol 06 (01) ◽  
pp. 1093-1101 ◽  
Author(s):  
Jensi R. ◽  
◽  
Wiselin Jiji G. ◽  
Keyword(s):  
Data Clustering ◽  
Bat Algorithm ◽  
Lévy Flight ◽  
Clustering Method ◽  
Levy Flight ◽  
Modified Bat Algorithm
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