An Advanced Clustering Algorithm (ACA) for Clustering Large Data Set to Achieve High Dimensionality

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Aman Toor
Aman Toor

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Cluster analysis method is one of the main analytical methods in data mining; this method of clustering algorithm will influence the clustering results directly. This paper proposes an Advanced Clustering Algorithm in order to solve this question, requiring a simple data structure to store some information [1] in every iteration, which is to be used in the next iteration. The Advanced Clustering Algorithm method avoids computing the distance of each data object to the cluster centers repeat, saving the running time. Experimental results show that the Advanced Clustering Algorithm method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the traditional algorithm. This paper includes Advanced Clustering Algorithm (ACA) and describes the experimental results and conclusions through experimenting with academic data sets.

16 Cites in Articles

References

  1. F Yuan,Meng Zhang,H,Dong (2004). A New Algorithm to Get the Initial Centroids.
  2. Sun Jigui,Liu Jie,Zhao Lianyu (2008). Clustering algorithms Research.
  3. Sun Shibao,Qin Keyun,; Jacobs,C Bean (2007). Research on Modified kmeans Data Cluster Algorithm.
  4. C Merz,P Murphy UCI Repository of Machine Learning Databases.
  5. A Fahim,A M Salem,F Torkey (2006). An efficient enhanced k-means clustering algorithm.
  6. Y Zhao,J Song (2001). GDILC: A grid-based density isoline clustering algorithm.
  7. Amanpreet Kaur Toor,Amarpreet Singh (2013). A Survey paper on recent clustering approaches in data mining.
  8. Amanpreet Kaur Toor,Amarpreet Singh (2013). An Advanced Clustering Algorithm (ACA) for Clustering Large Data Set to Achieve High Dimensionality.
  9. Z Huang (1998). Extensions to the k-means algorithm for clustering large data sets with categorical values.
  10. K Abdul Nazeer,M Sebastian,S Madhu Kumar (2009). A Heuristic <I>k</I>-Means Algorithm with Better Accuracy and Efficiency for Clustering Health Informatics Data.
  11. Aln Fred,Jmn Leitão (2000). Partitionalvs hierarchical clustering using a minimum grammar compexity approach.
  12. R Gelbard,I Spiegler (2000). Hempel's raven paradox: A positive approach to cluster analysis.
  13. Zhexue Huang (1997). Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values.
  14. C Ding,X He (2004). K-Nearest-Neighbor in data clustering: Incorporating local information into global optimization.
  15. Tian Zhang,Raghu Ramakrishnan,Miron Livny (1996). BIRCH.
  16. Derya Birant,Alp Kut (2007). ST-DBSCAN: An algorithm for clustering spatial–temporal data.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Aman Toor. 2014. \u201cAn Advanced Clustering Algorithm (ACA) for Clustering Large Data Set to Achieve High Dimensionality\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C2): .

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GJCST Volume 14 Issue C2
Pg. 71- 74
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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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May 15, 2014

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English

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Cluster analysis method is one of the main analytical methods in data mining; this method of clustering algorithm will influence the clustering results directly. This paper proposes an Advanced Clustering Algorithm in order to solve this question, requiring a simple data structure to store some information [1] in every iteration, which is to be used in the next iteration. The Advanced Clustering Algorithm method avoids computing the distance of each data object to the cluster centers repeat, saving the running time. Experimental results show that the Advanced Clustering Algorithm method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the traditional algorithm. This paper includes Advanced Clustering Algorithm (ACA) and describes the experimental results and conclusions through experimenting with academic data sets.

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An Advanced Clustering Algorithm (ACA) for Clustering Large Data Set to Achieve High Dimensionality

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