Hybrid data clustering approaches using bacterial colony optimization and k-means Article Swipe
J. Revathi
,
V. P. Eswaramurthy
,
P. Padmavathi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1757-899x/1070/1/012064
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1757-899x/1070/1/012064
Data clustering is a fashionable data analysis technique in the data mining. K-means is a popular clustering technique for solving a clustering problem. However, the k-means clustering technique extremely depends on the initial position and converges to a local optimum. On the other hand, the bacterial colony optimization (BCO) is a well-known recently proposed data clustering algorithm. However, it is a high computational cost to complete a given solution. Hence, this research paper proposes a new hybrid data clustering method for solving data clustering problem. The proposed hybrid data clustering algorithm is a combination of the BCO and K-means called BCO+KM clustering algorithm. The experimental result shows that the proposed hybrid BCO+KM data clustering algorithm reveal better cluster partitions.
Related Topics
Concepts
Cluster analysis
CURE data clustering algorithm
Correlation clustering
Canopy clustering algorithm
Data mining
Computer science
Data stream clustering
Determining the number of clusters in a data set
Clustering high-dimensional data
Single-linkage clustering
Fuzzy clustering
Algorithm
Artificial intelligence
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1757-899x/1070/1/012064
- OA Status
- diamond
- Cited By
- 13
- References
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3129245968
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3129245968Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1757-899x/1070/1/012064Digital Object Identifier
- Title
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Hybrid data clustering approaches using bacterial colony optimization and k-meansWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-02-01Full publication date if available
- Authors
-
J. Revathi, V. P. Eswaramurthy, P. PadmavathiList of authors in order
- Landing page
-
https://doi.org/10.1088/1757-899x/1070/1/012064Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1088/1757-899x/1070/1/012064Direct OA link when available
- Concepts
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Cluster analysis, CURE data clustering algorithm, Correlation clustering, Canopy clustering algorithm, Data mining, Computer science, Data stream clustering, Determining the number of clusters in a data set, Clustering high-dimensional data, Single-linkage clustering, Fuzzy clustering, Algorithm, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2023: 5, 2022: 4, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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11Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.experimental | 105 |
| abstract_inverted_index.optimization | 48 |
| abstract_inverted_index.computational | 63 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 93 |
| corresponding_author_ids | https://openalex.org/A5109706775 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I141431873 |
| citation_normalized_percentile.value | 0.86470094 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |