Clustering Validation of CLARA and K-Means Using Silhouette &amp; DUNN Measures on Iris Dataset

Gupta, Tanvi; Panda, Supriya P.

doi:10.1109/comitcon.2019.8862199

Cited by 35 publications

(23 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Figures 2,3,4,5,6,7 shows the wine dataset clustering for the algorithms k-means [20], nested mini-batch k-means [16], birch [26], mini-batch k-means [17], agglomerative clustering [15], DPCGS (our method) respectively. Figures 8,9,10,11,12,13 shows the wheat seeds dataset clustering for the algorithms k-means, nested mini-batch k-means, birch [26], mini-batch k-means, agglomerative clustering, DPCGS (our method) respectively. Figures 14,15,16,17,18,19,20 shows the iris dataset clustering for the algorithms k-means, nested minibatch k-means, birch, mini-batch k-means, agglomerative clustering, affinity propogation [3], DPCGS (our method) respectively.…”

Section: Results and Detailed Analysismentioning

confidence: 99%

“…However, if the two algorithms' scores are not comparable depending on the internal evaluation tests, it is not better to judge the algorithm. As an internal indicator for estimating the clusters outcome, we use the Silhouette coefficient [10] to calculate the average distance between data points and other data points on the same cluster and the average distance between clusters. To verify the algorithm's validity, the external evaluation, considered the gold standard for the testing process, takes the external data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data Points Clustering via Gumbel Softmax

Acharya

Zhang

2021

SN COMPUT. SCI.

View full text Add to dashboard Cite

Finding useful patterns in the dataset has been a fascinating topic, and one of the most researched problems in this area is identifying the cluster groups within the dataset. This research paper introduces a "new data clustering method" called Data Points Clustering via Gumbel Softmax (DPCGS) and demonstrates that it is suitable for clustering the data points datasets. We evaluate DPCGS efficiency and clustering quality through several experiments. Experiments show that statistically relevant clustering structures can be identified with our method, depending on the dataset. We also present a performance comparison table where we use datasets such as Wine, Wheat seeds, Iris, Wisconsin breast cancer and compare the DPCGS results with different benchmarking and recently proposed clustering algorithms such as Birch, K-Means, Affinity propagation, Agglomerative clustering, and Mini-batch K-Means and Nested mini-batch K-Means. Our method DPCGS performs better than most of the previously and recently proposed clustering algorithms.

show abstract

Section: Results and Detailed Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Points Clustering via Gumbel Softmax

Acharya

Zhang

2021

SN COMPUT. SCI.

View full text Add to dashboard Cite

show abstract

“…10, it can be included that k = 3 is a good choice; however, the elbow method does not always provide accurate results, so another clustering metric has to be checked to confirm the obtained result. To give intuition about k , the silhouette analysis has been implemented [21]. This method is used to determine the degree of separation between clusters [21].…”

Section: The Unsupervised Learning and Clustering Resultsmentioning

confidence: 99%

“…To give intuition about k , the silhouette analysis has been implemented [21]. This method is used to determine the degree of separation between clusters [21]. This method is generally implemented by calculating the average distance from all data points in the same cluster d i and in the closest cluster c i .…”

Section: The Unsupervised Learning and Clustering Resultsmentioning

confidence: 99%

Investigation on the scattering characteristics and unsupervised clustering of 3D printed samples

Elsaadouny

Barowski

Jebramcik

et al. 2020

Int. J. Microw. Wireless Technol.

View full text Add to dashboard Cite

In this work, the scattering characteristics of 3D-printed samples are being investigated by using a single-polarized and a cross-polarized radar system. The 3D-printed technology participates in a wide range of applications nowadays. The idea of synthetic aperture radar (SAR) has been utilized to investigate the reflected electromagnetic energy from the 3D-printed samples by setting each of the radar systems in a fixed position and the mounting sample on an x-y positioning table which has been used to achieve rectangular-scan mode for SAR. The data have been ported and processed by the matched filter approach. For better image interpretation, the data have been further processed by the median filter in order to reduce noise level while preserving the main image details. Afterwards, the data have been further investigated for determining and classifying any possible defects. This process has been accomplished by deploying the unsupervised learning concept to cluster the SAR responses into two groups, compromising the defected positions responses and the non-defected responses. The obtained results of both radar sensors have been compared and evaluated using different quality assessment factors. Moreover, unsupervised learning techniques have been investigated and the obtained results show a high degree of efficiency in clustering the SAR responses.

show abstract

“…Distribusi tersebut dapat menggunakan sudut 0 0 , 45 0 , 90 0 , 135 0 [14]. Ciri yang diperoleh dianaliais menggunakan metode K-mean Clustering untuk diketahui pola masing-masing dokumen cetak [15]. Metode K-mean clustering dalam memodelkan suatu data ciri secara unsupervised.…”

Section: Tinjauan Pustakaunclassified

Verifikasi Dokumen Cetak Menggunakan Metode Edge Detection-GLCM Dan K-Mean Clustering

Kurniati¹,

Huizen²

2020

dinamik

View full text Add to dashboard Cite

Dokumen cetak merupakan data digital yang tervisualisasi di kertas. Dokumen cetak mudah dibuat sekaligus dipalsukan. Cara mengetahui keasliannya dengan verifikasi ciri. Setiap dokumen cetak terdapat ciri unik berasal dari printer yang digunakan. Mengembangkan metode untuk menentukan keaslian dokumen cetak dari printer jenis dan tipe sama menjadi tantangan untuk diselesaikan pada penelitian ini. Proses verifikasi menggunakan tahapan akuisisi, ekstraksi, pengenalan pola dan hasil verifikasi. Data uji menggunakan 6 printer jenis dan tipe sama, masing-masing menggunakan 3 lembar sampel, untuk karakter dipilih 8. Hasilnya pengujian menunjukkan nilai EER (equal error rate) sebesar 0,18 dari nilai tersebut digunakan menentukan nilai ambang verifikasi yaitu 80% dari nilai kedekatan. Verifikasi dengan kedekatan diatas 80% berarti dokumen cetak dinyatakan asli (accepted) sedangkan kedekatan dibawah 80% berarti dokumen cetak dinyatakan palsu (rejected). Pengujian menggunakan 20 varian dokumen cetak diperoleh nilai kedekatan tertinggi 97,7% dan terendah 84,1%. Berdasarkan pengujian diperoleh suatu kesimpulan bahwa meskipun pemalsuan menggunakan printer dengan jenis dan tipe yang sama, model mampu memilah dan menentukan suatu dokumen cetak asli ataupun palsu.

show abstract

Clustering Validation of CLARA and K-Means Using Silhouette & DUNN Measures on Iris Dataset

Cited by 35 publications

References 4 publications

Data Points Clustering via Gumbel Softmax

Data Points Clustering via Gumbel Softmax

Investigation on the scattering characteristics and unsupervised clustering of 3D printed samples

Verifikasi Dokumen Cetak Menggunakan Metode Edge Detection-GLCM Dan K-Mean Clustering

Contact Info

Product

Resources

About