scholarly journals COMPARISON OF K-MEANS CLUSTERING METHOD AND K-MEDOIDS ON TWITTER DATA

2020 ◽  
Vol 4 (1) ◽  
pp. 189-202 ◽  
Author(s):  
Cahyani Oktarina ◽  
Khairil Anwar Notodiputro ◽  
Indahwati Indahwati
Keyword(s):  
Presidential Election ◽  
Optimal Number ◽  
Primary Data ◽  
The Political ◽  
Number Of Clusters ◽  
Political Issues ◽  
Twitter Data ◽  
Political Events ◽  
The Difference ◽  
Optimal Number Of Clusters

The presidential election is one of the political events that occur in Indonesia once in five years. Public satisfaction and dissatisfaction with political issues have led to an increase in the number of political opinion tweets. The purpose of this study is to examine the performance of the k-means and k-medoids method in the Twitter data and to tweet about the presidential election in 2019. The data used in this study are primary data taken from Muhyi's research, then mining the text against data obtained. Because this data has been processed by Muhyi to analyze the electability of the 2019 presidential candidate pairs, for this journal needs a preprocessing was carried out to analyze the tendency of tweets to side with the candidate pairs of one or two. The difference in the pre-processing of this research with previous research is that there is a cleaning of duplicate data and normalizing. The results of this study indicate that the optimal number of clusters resulting from the k-means method and the k-medoid method are different.

Clustering Count-based RNA Methylation Data Using a Nonparametric Generative Model

2018 ◽  
Vol 14 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Lin Zhang ◽  
Yanling He ◽  
Huaizhi Wang ◽  
Hui Liu ◽  
Yufei Huang ◽  
...  
Keyword(s):  
Clustering Analysis ◽  
Methylation Level ◽  
Optimal Number ◽  
Generative Model ◽  
Methylation Data ◽  
Sequencing Data ◽  
Number Of Clusters ◽  
Rna Methylation ◽  
Clustering Effect ◽  
Optimal Number Of Clusters

Background: RNA methylome has been discovered as an important layer of gene regulation and can be profiled directly with count-based measurements from high-throughput sequencing data. Although the detailed regulatory circuit of the epitranscriptome remains uncharted, clustering effect in methylation status among different RNA methylation sites can be identified from transcriptome-wide RNA methylation profiles and may reflect the epitranscriptomic regulation. Count-based RNA methylation sequencing data has unique features, such as low reads coverage, which calls for novel clustering approaches. <P><P> Objective: Besides the low reads coverage, it is also necessary to keep the integer property to approach clustering analysis of count-based RNA methylation sequencing data. <P><P> Method: We proposed a nonparametric generative model together with its Gibbs sampling solution for clustering analysis. The proposed approach implements a beta-binomial mixture model to capture the clustering effect in methylation level with the original count-based measurements rather than an estimated continuous methylation level. Besides, it adopts a nonparametric Dirichlet process to automatically determine an optimal number of clusters so as to avoid the common model selection problem in clustering analysis. <P><P> Results: When tested on the simulated system, the method demonstrated improved clustering performance over hierarchical clustering, K-means, MClust, NMF and EMclust. It also revealed on real dataset two novel RNA N6-methyladenosine (m6A) co-methylation patterns that may be induced directly by METTL14 and WTAP, which are two known regulatory components of the RNA m6A methyltransferase complex. <P><P> Conclusion: Our proposed DPBBM method not only properly handles the count-based measurements of RNA methylation data from sites of very low reads coverage, but also learns an optimal number of clusters adaptively from the data analyzed. <P><P> Availability: The source code and documents of DPBBM R package are freely available through the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/DPBBM/.

Het publiek in de tribune : vormen van politieke informatie

Res Publica ◽  
1975 ◽  
Vol 17 (4) ◽  
pp. 457-470
Author(s):  
Guido Dierickx
Keyword(s):  
Social Problems ◽  
Local Politics ◽  
Political Process ◽  
Political Conflict ◽  
Political Information ◽  
The Political ◽  
Political Issues ◽  
Discriminating Power ◽  
Political Events ◽  
Rules Of The Game

This contribution should be seen as an attempt to retrieve information from restcategories, such as «does not know» and «no answer».  From these, and from other data as well, we constructed 10, mostly summating, indexes of political ignorance. Among them is an index of objective ignorance, that is about political events, persons and situations.  The others aim at more subjective dimensions. Does the respondent feel informed about the political process : about government and party performance, partisan congeniality, modalities of voting, local politics social problems, political issues ?There seems to be some evidence in favor of the following hypotheses.1. The indexes tend to compensate each other: respondents who score low on one index, do not necessarily score low on the next one.2. I t is difficult to ascertain the validity of an index of objective ignorance. Moreover it does by no means express all the (relevant) dimensions of political information.3. A mong indexes of subjective ignorance one should distinguish between «policy» and «political» information ; the latter seems to refer to a situation where strictly political rules of the game, a.o. those of political conflict, prevail.4. Of all indexes the «political issues» index showed the most discriminating power, as well as the most expected associations.

Investigating cluster validation metrics for optimal number of clusters determination

2021 ◽  
pp. 1-16
Author(s):  
Aikaterini Karanikola ◽  
Charalampos M. Liapis ◽  
Sotiris Kotsiantis
Keyword(s):  
Real World ◽  
Optimal Number ◽  
Cluster Validation ◽  
Clustering Methods ◽  
Number Of Clusters ◽  
Validity Indices ◽  
Selection Of ◽  
Specific Distance ◽  
Optimal Number Of Clusters

In short, clustering is the process of partitioning a given set of objects into groups containing highly related instances. This relation is determined by a specific distance metric with which the intra-cluster similarity is estimated. Finding an optimal number of such partitions is usually the key step in the entire process, yet a rather difficult one. Selecting an unsuitable number of clusters might lead to incorrect conclusions and, consequently, to wrong decisions: the term “optimal” is quite ambiguous. Furthermore, various inherent characteristics of the datasets, such as clusters that overlap or clusters containing subclusters, will most often increase the level of difficulty of the task. Thus, the methods used to detect similarities and the parameter selection of the partition algorithm have a major impact on the quality of the groups and the identification of their optimal number. Given that each dataset constitutes a rather distinct case, validity indices are indicators introduced to address the problem of selecting such an optimal number of clusters. In this work, an extensive set of well-known validity indices, based on the approach of the so-called relative criteria, are examined comparatively. A total of 26 cluster validation measures were investigated in two distinct case studies: one in real-world and one in artificially generated data. To ensure a certain degree of difficulty, both real-world and generated data were selected to exhibit variations and inhomogeneity. Each of the indices is being deployed under the schemes of 9 different clustering methods, which incorporate 5 different distance metrics. All results are presented in various explanatory forms.

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