Comparative Study of Single Linkage, Complete Linkage, and Ward Method of Agglomerative Clustering

Vijaya, Vijaya; Sharma, Shweta; Batra, Neha

doi:10.1109/comitcon.2019.8862232

Cited by 53 publications

(36 citation statements)

References 9 publications

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“…One cluster can be decided to be separated or merged at a certain level based on the similarity criteria between the clusters are met. The agglomerative hierarchical clustering approach begins with separate data input which are grouped gradually to form a single group (bottom-up process) [28][29] while the divisive hierarchical clustering approach should be done in a top-down manner starting from one cluster which is separated into smallest element of the data set. Hierarchical clustering hybrid with other data mining methods for input optimization purposes [30].…”

Section: ) Association Rulementioning

confidence: 99%

A Survey of Data Mining Techniques for Smart Museum Applications

Puspasari

Ermatita

2021

Jurnal Informatika

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This research aims to find out what data mining techniques are effectively implemented in museums and what application trends are currently being used to improve museum performance towards modern museums based on intelligent system technology. The review was carried out on a number of articles found in journals and proceedings in the 2004-2020 period. It is found that the majority of data mining techniques are implemented in museum virtual guide applications, recommender systems, collection clustering and classification system, and visitor behaviour prediction application. Data classification, clustering, and prediction technique commonly used for museum application. Collections with historical and artistic value contain a lot of knowledge making data mining an important technique to be included in various applications in museums so that they can have an impact on the achievement of museum goals not only in the fields of education and culture but also economics and business.

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Section: ) Association Rulementioning

confidence: 99%

A Survey of Data Mining Techniques for Smart Museum Applications

Puspasari

Ermatita

2021

Jurnal Informatika

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“…Although we investigated traditional hierarchical methods to design the framework for Twitter sentiment analysis, yet these are popular among the research community. For example, these techniques have been investigated in recent times even during years 2018 and 2019 [40]- [42]. Novelty of the present study stems from the fact that a) hierarchical clustering is investigated first time thoroughly for (Twitter) sentiment analysis, and b) first time an ensemble of clustering techniques is created which achieves comparable performance to the widely studied classification techniques.…”

Section: Have Explored Twitter Datamentioning

confidence: 99%

“…They used k-means clustering algorithm as a baseline and showed that the algorithm was not encouraging for sentiment analysis. Recently, hierarchical agglomerative clustering has been investigated in [40] on real time shopping data. Better performance of CL and Ward's method is reported.…”

Section: Related Workmentioning

confidence: 99%

A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis

et al. 2020

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Twitter sentiment analysis is a challenging problem in natural language processing. For this purpose, supervised learning techniques have mostly been employed, which require labeled data for training. However, it is very time consuming to label datasets of large size. To address this issue, unsupervised learning techniques such as clustering can be used. In this study, we explore the possibility of using hierarchical clustering for twitter sentiment analysis. Three hierarchical-clustering techniques, namely single linkage (SL), complete linkage (CL) and average linkage (AL), are examined. A cooperative framework of SL, CL and AL is built to select the optimal cluster for tweets wherein the notion of optimal-cluster selection is operationalized using majority voting. The hierarchical clustering techniques are also compared with k-means and two state-of-the-art classifiers (SVM and Naïve Bayes). The performance of clustering and classification is measured in terms of accuracy and time efficiency. The experimental results indicate that cooperative clustering based on majority voting approach is robust in terms of good quality clusters with tradeoff of poor time efficiency. The results also suggest that the accuracy of the proposed clustering framework is comparable to classifiers which is encouraging. INDEX TERMS Cooperative clustering, majority voting, sentiment analysis, twitter sentiment analysis.

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“…The grouping technique that will be used is complete linkage. This technique combines clusters according to the distance between the farthest members of the two clusters [18]. The technique starts by making all points as individual clusters, then combining the two points that have a minimum distance.…”

Section: Classificationmentioning

confidence: 99%

“…The points that have the maximum Euclidean distance from the cluster must be combined with the next cluster. The process of finding the euclidean distance must be repeated until there is only one cluster with centroid i [18]. Data that has been labelled will be classified using deep learning.…”

Section: D(pq)=√(q 1 -P 1 )mentioning

confidence: 99%

Mental Disorder Detection via Social Media Mining using Deep Learning

Kholifah

Syarif

Badriyah

2020

KINETIK

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Mental disorders are a disease that cannot be physically seen, so diagnosing someone with a mental disorder is not easy. Due to the imperceptible nature of mental disorders, diagnosing a patient with a mental disorder is a challenging task. Therefore, detection in people with mental disorders can be done by looking at the symptoms they experience. One symptom in patients with mental disorders is solitude. Patients with mental disorders feel indifferent to their environment and focus on what is happening to them. Therefore, the patient looks for a place that can accommodate his feelings. Twitter is one of the most widely used media in measuring one's personality through everyday statements. The symptoms as suggested by psychologists can be explored more broadly using Natural Languages Processing. In this case, it takes a lexicon containing keywords that can show symptoms of depression. This study uses five criteria as a measure of mental health in a statement: sentiment, basic emotions, the use of personal pronouns, absolutist words, and negative words. The results show that the use of sentiments, emotions, and negative words in a statement is very influential in determining the level of depression. A depressed person more often uses negative words that indicate his self-despair, prolonged sadness, even suicidal thoughts (e.g. "sadly”, “scared”, “die”, “suicide”). In the classification process, using LSTM Deep Learning obtained an accuracy of 70.89%; precision of 50.24%; recall 70.89%.

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Comparative Study of Single Linkage, Complete Linkage, and Ward Method of Agglomerative Clustering

Cited by 53 publications

References 9 publications

A Survey of Data Mining Techniques for Smart Museum Applications

A Survey of Data Mining Techniques for Smart Museum Applications

A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis

Mental Disorder Detection via Social Media Mining using Deep Learning

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