Application of hybrid clustering using parallel k-means algorithm and DIANA algorithm

Umam, Khoirul; Bustamam, Alhadi; Lestari, Dian

doi:10.1063/1.4978993

Cited by 8 publications

(10 citation statements)

References 4 publications

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“…In order to improve the accuracy of software size estimation, Prokopov et al [9] proposed a new clustering method based on use case points, Compared with clustering algorithms such as K-Means, the evaluation of clustering results using indexes such as CHI and SC. Umam et al [10] proposed a hybrid clustering method based on K-Means clustering and hierarchical clustering. The sequence of DNA was used as a feature to cluster, the DBI was used to evaluate clustering results.…”

Section: Related Workmentioning

confidence: 99%

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Analysis and improvement of evaluation indexes for clustering results

Zhong¹,

Zhang²,

Jia³

2020

EAI Endorsed Transactions on Collaborative Computing

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Clustering algorithm is the main field in collaborative computing of social network. How to evaluate clustering results accurately has become a hot spot in clustering algorithm research. Commonly used evaluation indexes are SC, DBI and CHI. There are two shortcomings in the calculation of three indexes. (1) Keep the number of clusters and the objects in the cluster unchanged. When transforming the feature vector, the three indexes will change greatly; (2) Keep the feature vector and the number of clusters unchanged. When changing the objects in the cluster, the three indexes will change tinily. This shows that the three indexes unable to evaluate the clustering results very well. Therefore, based on the calculation process of the three indexes, the paper proposes new three indexes -NSC, NDBI and NCHI. Through testing on standard data sets, three new indexes can better evaluate clustering results.

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Section: Related Workmentioning

confidence: 99%

“…When the vector elements change in interval [0, +∞], the calculated value of Cos() will be stable in interval [0,1]. Suppose that the relationship between vectors a and b is calculated by Cos(), as shown in formula (10).…”

Section: Solutions To Problemsmentioning

confidence: 99%

Analysis and improvement of evaluation indexes for clustering results

Zhong¹,

Zhang²,

Jia³

2020

EAI Endorsed Transactions on Collaborative Computing

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“…Step 2 Determine the cluster center (1) Calculate the threshold of  and  according to the equations (4) and (5).…”

Section: Pt-cfsfdp Algorithmmentioning

confidence: 99%

“…In the hierarchy-based clustering, the dataset is decomposed hierarchically according to a certain method, until the specified conditions are satisfied. According to the different classification principles, it can be divided into cohesion and splitting methods, AGNES [4] algorithm and DIANA [5] algorithm are one of the representatives. Such algorithms may not be well scalable because of poor or poorly selected clusters due to merging or splitting.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Density Peak Clustering Algorithm Based on OpenMP

Qiu¹,

Wang²

2018

JSW

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The density peak clustering algorithm(CFSFDP) is a new clustering algorithm that implements simple, clustering non-spherical data sets. The algorithm needs artificial selection of clustering center, it is difficult to get the actual clustering centers accurately and can not effectively deal with various data sets. And the density calculation process has nonlinear time complexity. In response to the above problems, a threshold-based parallel optimization CFSFDP (PT-CFSFDP) algorithm is proposed, which sets the threshold for the local density of samples and the distance to the points with higher local density, the sample point is selected as the cluster center when the parameter is greater than the threshold. The distance matrix is optimized in parallel with OpenMP. Experiments show that the PT-CFSFDP algorithm can get the clustering center accurately, the accuracy of the clustering results is up to 94% and the speedup of the algorithm is up to 4.25.

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“…Oleh karena itu, pada N-mers Frequency ditentukan nilai N = 3, sehingga dimensi data akan menjadi 4 3 = 64. Dengan kata lain, data akan memiliki 64 dimensi, yaitu: AAA, AAC, AAT, AAG, ACA, ACC, ACT, ACG, ATA, ATC, ATT, ATG, AGA, AGC, AGT, AGG, CAA, CAC, CAT, CAG, CCA, CCC, CCT, CCG, CTA, CTC, CTT, CTG, CGA, CGC, CGT, CGG, GAA, GAC, GAT, GAG, GCA, GCC, GCT, GCG, GTA, GTC, GTT, GTG, GGA, GGC, GGT, GGG, TAA, TAC, TAT, TAG, TCA, TCC, TCT, TCG, TTA, TTC, TTG, TGA, TGC, TGT, TGG, TTT[11].…”

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Penggunaan N-mers Frequency pada Analisis Barisan DNA

Umam

Sagara

2020

Jambura J. Math

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Salah satu metode untuk menganalisis barisan DNA adalah menggunaan N-mers Frequency. N-mers Frequency termasuk metode data mining pada barisan DNA, dimana barisan DNA yang merupakan data string “ACGT” akan diubah menjadi data numerik. N-mers Frequency pada tulisan ini menggunakan N = 3. Hal ini disebabkan karena pada proses sintesis protein, tRNA akan membawa tiga basa nekleotida (anti kodon) yang akan dipasangkan dengan tiga basa nekleotida (kodon) pada pita mRNA. Dalam hal ini mRNA dibentuk dari duplikasi barisan DNA. Studi ini dilakukan untuk mengetahui akurasi dari penggunaan N-mers Frequency. Untuk menghitung Akurasi penggunaan N-mers Frequency, dilakukan tahapan seperti berikut: (1) pengumpulan data barisan DNA, (2) N-mers Frequency, (3) matriks jarak, (4) pengelompokan menggunakan algoritma K-means++, PAM, AGNES, dan DIANA, (5) menghitung akurasi, dan (6) kesimpulan. Akurasi dari Penggunaan N-mers Frequency pada penelitian ini adalah 100%, dengan menggunakan data 100 barisan DNA yang telah diketahui jenisnya, yaitu: virus HPV, virus Ebola, virus Marburg, dan virus Zika.

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Application of hybrid clustering using parallel k-means algorithm and DIANA algorithm

Cited by 8 publications

References 4 publications

Analysis and improvement of evaluation indexes for clustering results

Analysis and improvement of evaluation indexes for clustering results

Optimization of Density Peak Clustering Algorithm Based on OpenMP

Penggunaan N-mers Frequency pada Analisis Barisan DNA

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