Performance of Internal Cluster Validations Measures For Evolutionary Clustering

Nerurkar, Pranav; Pavate, Aruna; Shah, Mansi; Jacob, Samuel I.

doi:10.1007/978-981-13-1513-8_32

Cited by 12 publications

(8 citation statements)

References 13 publications

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“…Sehingga pada penelitian ini, kluster validation yang digunakan hanya Internal kluster validation karena tidak terdapat label eksternal yang diketahui untuk dijadikan acuan pada external cluster validation. Fungsi yang digunakan adalah cluster.stats dari library fpc pada RStudio [12].…”

Section: E Proses 5: Validate Resultsunclassified

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K-Means Clustering Video Trending di Youtube Amerika Serikat

Widjaja¹,

Oetama

2020

ULTIMA InfoSys

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Youtube is the most popular video platform in the world today. Successful YouTubers can create videos that are widely viewed by many Youtube users around the world. A lot of viral videos on Youtube came from the United States. But, making viral videos on Youtube is a tough challenge, both for seasoned YouTubers and especially for new YouTubers. This research focuses on discovering the properties of these viral videos by clustering them into distinct clusters. K-Means algorithm is used for the clustering process. The purpose of this clustering process is to look for patterns in the data that were previously unseen. The result shows that the videos are divided into three clusters which are built from 3 variables; views, likes and dislikes. The patterns and insights found in this study can be useful for aspiring video makers who want to achieve success as a Youtuber.

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Section: E Proses 5: Validate Resultsunclassified

“…Average Between didapatkan dengan menjalankan perintah stat$average.between pada RStudio. Average within didapatkan dengan menjalankan perintah stat$average.within pada RStudio [12].…”

Section: Issn 2085-4579unclassified

K-Means Clustering Video Trending di Youtube Amerika Serikat

Widjaja¹,

Oetama

2020

ULTIMA InfoSys

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“…The clustering performance is measured in two datasets, Genia and Biotext. Document clustering is performed using the k-mean clustering method of P (T | D).There are two methods for clustering validation, and internal validation method is more accurate than external validation [50]. We use the internal validation method of the Calinski-Harabasz index to evaluate multiple topics and clusters.…”

Section: Clustering Of Documentsmentioning

confidence: 99%

A novel multiple kernel fuzzy topic modeling technique for biomedical data

et al. 2022

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Background Text mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data is in text format. Topic modeling is one of the popular methods among text mining techniques used to discover hidden semantic structures, so called topics. However, discovering topics from biomedical data is a challenging task due to the sparsity, redundancy, and unstructured format. Methods In this paper, we proposed a novel multiple kernel fuzzy topic modeling (MKFTM) technique using fusion probabilistic inverse document frequency and multiple kernel fuzzy c-means clustering algorithm for biomedical text mining. In detail, the proposed fusion probabilistic inverse document frequency method is used to estimate the weights of global terms while MKFTM generates frequencies of local and global terms with bag-of-words. In addition, the principal component analysis is applied to eliminate higher-order negative effects for term weights. Results Extensive experiments are conducted on six biomedical datasets. MKFTM achieved the highest classification accuracy 99.04%, 99.62%, 99.69%, 99.61% in the Muchmore Springer dataset and 94.10%, 89.45%, 92.91%, 90.35% in the Ohsumed dataset. The CH index value of MKFTM is higher, which shows that its clustering performance is better than state-of-the-art topic models. Conclusion We have confirmed from results that proposed MKFTM approach is very efficient to handles to sparsity and redundancy problem in biomedical text documents. MKFTM discovers semantically relevant topics with high accuracy for biomedical documents. Its gives better results for classification and clustering in biomedical documents. MKFTM is a new approach to topic modeling, which has the flexibility to work with a variety of clustering methods.

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“…Particularly, it deals with data sets whose clusters present asymmetry in their geometries. The work by Nerurkar et al 30 focuses on the use of internal validation criteria, in particular the BetaCV and Dunn internal indices, as cost functions of swarm optimizers. In a similar way, Reséndiz, Castro, and Leal 31 propose the use of clustering validation indices and maximum entropy as cost functions to quantify the quality of automatic image segmentation.…”

Section: Related Workmentioning

confidence: 99%

New internal clustering validation measure for contiguous arbitrary‐shape clusters

Rojas-Thomas

Santos

2021

Int J Intell Syst

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In this study a new internal clustering validation index is proposed. It is based on a measure of the uniformity of the data in clusters. It uses the local density of each cluster, in particular, the normalized variability of the density within the clusters to find the ideal partition. The new validity measure allows it to capture the spatial pattern of the data and obtain the right number of clusters in an automatic way. This new approach, unlike the traditional one that usually identifies wellseparated compact clouds, works with arbitrary-shape clusters that may be contiguous or even overlapped.The proposed clustering measure has been evaluated on nine artificial data sets, with different cluster distributions and an increasing number of classes, on three highly nonlinear data sets, and on 17 real data sets. It has been compared with nine well-known clustering validation indices with very satisfactory results. This proves that including density in the definition of clustering validation indices may be useful to identify the right partition of arbitrary-shape and different-size clusters.

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Performance of Internal Cluster Validations Measures For Evolutionary Clustering

Cited by 12 publications

References 13 publications

K-Means Clustering Video Trending di Youtube Amerika Serikat

K-Means Clustering Video Trending di Youtube Amerika Serikat

A novel multiple kernel fuzzy topic modeling technique for biomedical data

New internal clustering validation measure for contiguous arbitrary‐shape clusters

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