Analysis and Exploration of Clustering Algorithms for New Student Segmentation Article Swipe
Langgeng Restuono
,
Andysah Putera Utama Siahaan
,
Rian Farta Wijaya
,
Zulham Sitorus
,
Muhammad Naeem Iqbal
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.61306/ijecom.v3i1.61
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.61306/ijecom.v3i1.61
Clustering analysis is a crucial technique in data processing and pattern understanding. In this study, we compare the clustering results using the k-Means algorithm with two different approaches to centroid initialization: random centroids and manual centroids. The dataset consists of three observed variables. The analysis results indicate significant differences in centroid placement and cluster formation between the two approaches. The random centroid approach yields three clusters with centroids located at different coordinates: Cluster 1 [1.76, 2.5, 10.88], Cluster 2 [1.60, 1.87, 2.23], and Cluster 3 [1.64, 1.568, 15.88]. On the other hand, the manual centroid approach generates three clusters with centroids manually specified: Cluster 1 [1.64, 1.81, 14.84], Cluster 2 [1.61, 1.901, 2.04], and Cluster 3 [1.75, 1.7, 6.8]. The analysis and interpretation of these differences highlight the sensitivity of the k-Means algorithm to centroid initialization. The implications of these findings provide insights into the importance of selecting the appropriate initialization method in clustering analysis to ensure consistent and meaningful results. This research makes a significant contribution to understanding the factors influencing clustering results and can serve as a guide for researchers and practitioners in choosing clustering approaches that are suitable for their data and analytical goals.
Related Topics
Concepts
Centroid
Initialization
Cluster analysis
Cluster (spacecraft)
Computer science
Data mining
k-medians clustering
k-means clustering
Segmentation
Pattern recognition (psychology)
Artificial intelligence
Algorithm
Fuzzy clustering
CURE data clustering algorithm
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.61306/ijecom.v3i1.61
- https://ijecom.org/index.php/IJECOM/article/download/61/63
- OA Status
- diamond
- Cited By
- 2
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399172445
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399172445Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.61306/ijecom.v3i1.61Digital Object Identifier
- Title
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Analysis and Exploration of Clustering Algorithms for New Student SegmentationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-05-30Full publication date if available
- Authors
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Langgeng Restuono, Andysah Putera Utama Siahaan, Rian Farta Wijaya, Zulham Sitorus, Muhammad Naeem IqbalList of authors in order
- Landing page
-
https://doi.org/10.61306/ijecom.v3i1.61Publisher landing page
- PDF URL
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https://ijecom.org/index.php/IJECOM/article/download/61/63Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://ijecom.org/index.php/IJECOM/article/download/61/63Direct OA link when available
- Concepts
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Centroid, Initialization, Cluster analysis, Cluster (spacecraft), Computer science, Data mining, k-medians clustering, k-means clustering, Segmentation, Pattern recognition (psychology), Artificial intelligence, Algorithm, Fuzzy clustering, CURE data clustering algorithm, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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14Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works_count | 14 |
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| abstract_inverted_index.2 | 78, 109 |
| abstract_inverted_index.3 | 84, 115 |
| abstract_inverted_index.a | 3, 164, 178 |
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| abstract_inverted_index.for | 180, 191 |
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| abstract_inverted_index.2.5, | 75 |
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| abstract_inverted_index.1.81, | 106 |
| abstract_inverted_index.1.87, | 80 |
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| abstract_inverted_index.1.901, | 111 |
| abstract_inverted_index.2.04], | 112 |
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| abstract_inverted_index.[1.60, | 79 |
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| abstract_inverted_index.10.88], | 76 |
| abstract_inverted_index.14.84], | 107 |
| abstract_inverted_index.15.88]. | 87 |
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| cited_by_percentile_year.min | 90 |
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| institutions_distinct_count | 5 |
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| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |