Cube Sampled K-Prototype Clustering for Featured Data Article Swipe
Seemandhar Jain
,
Aditya Shastri
,
Kapil Ahuja
,
Yann Busnel
,
Navneet Pratap Singh
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/indicon52576.2021.9691727
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/indicon52576.2021.9691727
Clustering large amount of data is becoming increasingly important in the current times. Due to the large sizes of data, clustering algorithm often take too much time. Sampling this data before clustering is commonly used to reduce this time. In this work, we propose a probabilistic sampling technique called cube sampling along with K-Prototype clustering. Cube sampling is used because of its accurate sample selection. K-Prototype is most frequently used clustering algorithm when the data is numerical as well as categorical (very common in today's time). The novelty of this work is in obtaining the crucial inclusion probabilities for cube sampling using Principal Component Analysis (PCA). Experiments on multiple datasets from the UCI repository demonstrate that cube sampled K-Prototype algorithm gives the best clustering accuracy among similarly sampled other popular clustering algorithms (K-Means, Hierarchical Clustering (HC), Spectral Clustering (SC)). When compared with unsampled K-Prototype, K-Means, HC and SC, it still has the best accuracy with the added advantage of reduced computational complexity (due to reduced data size).
Related Topics
Concepts
Cluster analysis
CURE data clustering algorithm
Computer science
Data stream clustering
Correlation clustering
Data mining
Canopy clustering algorithm
Sampling (signal processing)
Single-linkage clustering
Clustering high-dimensional data
Fuzzy clustering
Hierarchical clustering
Principal component analysis
Categorical variable
Cube (algebra)
DBSCAN
Pattern recognition (psychology)
Artificial intelligence
Mathematics
Machine learning
Combinatorics
Filter (signal processing)
Computer vision
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/indicon52576.2021.9691727
- OA Status
- green
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3193946959
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3193946959Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/indicon52576.2021.9691727Digital Object Identifier
- Title
-
Cube Sampled K-Prototype Clustering for Featured DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-19Full publication date if available
- Authors
-
Seemandhar Jain, Aditya Shastri, Kapil Ahuja, Yann Busnel, Navneet Pratap SinghList of authors in order
- Landing page
-
https://doi.org/10.1109/indicon52576.2021.9691727Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2108.10262Direct OA link when available
- Concepts
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Cluster analysis, CURE data clustering algorithm, Computer science, Data stream clustering, Correlation clustering, Data mining, Canopy clustering algorithm, Sampling (signal processing), Single-linkage clustering, Clustering high-dimensional data, Fuzzy clustering, Hierarchical clustering, Principal component analysis, Categorical variable, Cube (algebra), DBSCAN, Pattern recognition (psychology), Artificial intelligence, Mathematics, Machine learning, Combinatorics, Filter (signal processing), Computer visionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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15Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2021-12-19 |
| publication_year | 2021 |
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