scholarly journals Implementasi Metode Improved K-Means dengan Algoritma Dbscan untuk Pengelompokan Film

2020 ◽  
Vol 6 (01) ◽  
pp. 1-8
Author(s):  
Muhammad Muhajir ◽  
Annisa Ayunda Permata Sari

The Indonesian film industry continues to experience an increase seen from the number of films that appear in theaters today with a box office increase of 28 percent each year in the past four years. Internet Movie Database (IMDb) is a website that provides information about films around the world, including the people involved in it from actors, directors, writers to makeup artists and soundtracks. In this case the researcher wants to conduct research on the characteristics of the film and the factors that make a film to be included in the IMDb Top 250. The data used in this study uses scraped data from the website. The method used is a non-hierarchical clustering method, namely kmeans and Dbscan. Where the Dbscan algorithm is used to determine the optimum number of clusters then proceed by grouping data based on centroids with k-means algorithm. From the analysis it was found that the factors that could influence a film included in the IMDB Top 250 were duration, number of votes, and films directed by Rajkumar Hirani and the optimal number of clusters using Dbscan algorithm obtained six clusters. With the improved k-means algorithm, the accuracy value for the cluster results is 87.2%.

Author(s):  
Aashish kumar, Et. al.

Software-Defined Networking is one of the most revolutionary and prominent technology in the field of networking. It solves the problem that our traditional network faces. Still it can face a problem of bottleneck and can be overloaded. To overcome this issue, various researcher has it given various works but they are based on two or three-parameter to perform load balancing and also they are static or dynamic. We have proposed an intelligent technique that forwards the packet i.e. TCP/UDP packet traffic based on several parameters (based on 12 parameters discussed in the latter part of this section). Based on these parameters, we have applied the trained machine using KMeans [1] and DBSCAN [2] clustering algorithm and also determine the optimal number of clusters. We have tested it on the huge number of packet that are 5000, 10000, 20000, 50000, 100000, 10000000.We have also compared there results of the KMeans and DBSCAN algorithm and also discussed researchers view


2010 ◽  
Vol 30 (8) ◽  
pp. 1995-1998 ◽  
Author(s):  
Shi-bing ZHOU ◽  
Zhen-yuan XU ◽  
Xu-qing TANG

2018 ◽  
Vol 14 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Lin Zhang ◽  
Yanling He ◽  
Huaizhi Wang ◽  
Hui Liu ◽  
Yufei Huang ◽  
...  

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/.


2020 ◽  
Vol 45 (3) ◽  
pp. 241-248
Author(s):  
Engin Yilmaz ◽  
Yakut Akyön ◽  
Muhittin Serdar

AbstractCOVID-19 is the third spread of animal coronavirus over the past two decades, resulting in a major epidemic in humans after SARS and MERS. COVID-19 is responsible of the biggest biological earthquake in the world. In the global fight against COVID-19 some serious mistakes have been done like, the countries’ misguided attempts to protect their economies, lack of international co-operation. These mistakes that the people had done in previous deadly outbreaks. The result has been a greater economic devastation and the collapse of national and international trust for all. In this constantly changing environment, if we have a better understanding of the host-virus interactions than we can be more prepared to the future deadly outbreaks. When encountered with a disease which the causative is unknown, the reaction time and the precautions that should be taken matters a great deal. In this review we aimed to reveal the molecular footprints of COVID-19 scientifically and to get an understanding of the pandemia. This review might be a highlight to the possible outbreaks.


M/C Journal ◽  
1998 ◽  
Vol 1 (1) ◽  
Author(s):  
Joseph Crawfoot

Cities are an important symbol of our contemporary era. They are not just places of commerce, but are emblems of the people who live within them. A significant feature of cities are their meeting places; areas that have either been designed or appropriated by the people. An example of this is the café. Cafés hold a unique place in history, as sites that have witnessed the growth of revolution, relationships great and small, between people and ideas, and more recently, technology. Computers are transcending their place in the private home or office and are now finding their way into café culture. What I am suggesting is that this is bringing about a new way of understanding how cafés foster community and act as media for social interaction. To explore this idea further I will look at the historical background of the café, particularly within Parisian culture. For W. Scott Haine, cities such as Paris have highly influential abilities. As he points out "the Paris milieu determined the consciousness of workers as much as their labor" (114). While specifically related to Paris, Haine is highlighting an important aspect in the relationship between people and the built environment. He suggests that buildings and streets are not just inanimate objects, but structures that shape our habits and our beliefs. Towards the middle of the nineteenth century, Paris was developing a new cultural level, referred to as Bohemia. Derived from the French word for Gypsy (Seigel 5) it was used to denote a class of people who in the eyes of Honoré de Balzac were the talent of the future (Seigel 4). People who would be diplomats, artists, journalists, soldiers, who at that moment existed in a transient state with much social but little material wealth. Emerging within this Bohemian identity were the bourgeois. They were individuals who led a working class existence, they usually held property but more importantly they helped provide the physical environment for Bohemian culture to flourish. Bourgeois society had the money to patronize Bohemian artists. As Seigel says "Bohemian and bourgeois were -- and are -- parts of a single field: they imply, require, and attract each other" (5). Cafés were a site of symbiosis between these two groups. As Seigel points out they were not so much established to create a Bohemian world away from the reality of working life, but to provide a space were the predominantly bourgeois clientèle could be entertained (216). These ideas of entertainment saw the rise of the literary café, a venue not just for drinking and socialization but where potential writers and orators could perform for an audience. Contemporary society has seen a strong decline in Bohemian culture, with the (franchised) café being appropriated by the upper class as a site of lattes and mud cake. Recent developments in Internet technology however have prompted a change in this trend. Whereas in the past cafés had brought about a symbiosis between the classes of Bohemian and bourgeois society they are now becoming sites that foster relationships between the middle class and computer technology. Computers and the Internet have their origins within a privileged community, of government departments, defence forces and universities. It is only in the past three years that Internet technology has moved out of a realm of expert knowledge to achieve a broad level of usage in the average household. Certain barriers still exist though in terms of a person's ability to gain access to this medium. Just as Bohemian culture arose out of a population of educated people lacking skills of manual labor and social status (Seigel 217), computers and Internet culture offer a means for people to go beyond their social boundaries. Cafés were sites for Bohemians to transcend the social, political, and economic dictates that had shaped their lives. In a similar fashion the Internet offers a means for people to explore beyond their physical world. Internet cafés have been growing steadily around the world. What they represent is a change in the concept of social interaction. As in the past with the Paris café and the exchange of ideas, Internet cafés have become places were people can interact not just on a face-to-face basis but also through computer-mediated communication. What this points to is a broadening in the idea of the café as a medium of social interaction. This is where the latte and mud cake trend is beginning to break down. By placing Internet technology within cafés, proprietors are inviting a far greater section of the community within their walls. While these experiences still attract a price tag they suggest a change in the idea that would have seen both the café and the Internet as commodities of the élite. What this is doing is re-invigorating the idea of the streets belonging to the middle class and other sub-cultures, allowing people access to space so that relationships and communities can be formed. References Haine, W. Scott. The World of the Paris Cafe: Sociability amongst the French Working Class 1789 - 1914. Baltimore: Johns Hopkins UP, 1996. Seigel, Jerrold. Bohemian Paris: Culture, Politics and the Boundaries of Bourgeois Life, 1830 - 1930. New York: Penguin Books, 1987. Citation reference for this article MLA style: Joseph Crawfoot. "Cybercafé, Cybercommunity." M/C: A Journal of Media and Culture 1.1 (1998). [your date of access] <http://www.uq.edu.au/mc/9807/cafe.php>. Chicago style: Joseph Crawfoot, "Cybercafé, Cybercommunity," M/C: A Journal of Media and Culture 1, no. 1 (1998), <http://www.uq.edu.au/mc/9807/cafe.php> ([your date of access]). APA style: Joseph Crawfoot. (1998) Cybercafé, cybercommunity. M/C: A Journal of Media and Culture 1(1). <http://www.uq.edu.au/mc/9807/cafe.php> ([your date of access]).


2021 ◽  
pp. 1-16
Author(s):  
Aikaterini Karanikola ◽  
Charalampos M. Liapis ◽  
Sotiris Kotsiantis

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.


Sign in / Sign up

Export Citation Format

Share Document