Analysis of Brain fMRI Data via Topological Data Clustering Method IoPS

2021 ◽  
Author(s):  
Taekgeun Jung ◽  
Hong Seo Ryoo
Keyword(s):  
Data Clustering ◽  
Fmri Data ◽  
Clustering Method ◽  
Brain Fmri ◽  
Topological Data

scholarly journals KOMPARASI METODE CLUSTERING K-MEANS DAN K-MEDOIDS DENGAN MODEL FUZZY RFM UNTUK PENGELOMPOKAN PELANGGAN

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Elly Muningsih - AMIK BSI Yogyakarta
Keyword(s):  
Data Clustering ◽  
Small Data ◽  
Clustering Methods ◽  
Monetary Model ◽  
Clustering Method ◽  
Online Sales ◽  
Rfm Model ◽  
Potential Customers ◽  
Cluster 2 ◽  
Better Than

Abstract ~ The K-Means method is one of the clustering methods that is widely used in data clustering research. While the K-Medoids method is an efficient method used for processing small data. This study aims to compare two clustering methods by grouping customers into 3 clusters according to their characteristics, namely very potential (loyal) customers, potential customers and non potential customers. The method used in this study is the K-Means clustering method and the K-Medoids method. The data used is online sales transaction. The clustering method testing is done by using a Fuzzy RFM (Recency, Frequenty and Monetary) model where the average (mean) of the third value is taken. From the data testing is known that the K-Means method is better than the K-Medoids method with an accuracy value of 90.47%. Whereas from the data processing carried out is known that cluster 1 has 16 members (customers), cluster 2 has 11 members and cluster 3 has 15 members. Keywords : clustering, K-Means method, K-Medoids method, customer, Fuzzy RFM model. Abstrak ~ Metode K-Means merupakan salah satu metode clustering yang banyak digunakan dalam penelitian pengelompokan data. Sedangkan metode K-Medoids merupakan metode yang efisien digunakan untuk pengolahan data yang kecil. Penelitian ini bertujuan untuk membandingkan atau mengkomparasi dua metode clustering dengan cara mengelompokkan pelanggan menjadi 3 cluster sesuai dengan karakteristiknya, yaitu pelanggan sangat potensial (loyal), pelanggan potensial dan pelanggan kurang (tidak) potensial. Metode yang digunakan dalam penelitian ini adalah metode clustering K-Means dan metode K-Medoids. Data yang digunakan adalah data transaksi penjualan online. Pengujian metode clustering yang dilakukan adalah dengan menggunakan model Fuzzy RFM (Recency, Frequenty dan Monetary) dimana diambil rata-rata (mean) dari nilai ketiga tersebut. Dari pengujian data diketahui bahwa metode K-Means lebih baik dari metode K-Medoids dengan nilai akurasi 90,47%. Sedangkan dari pengolahan data yang dilakukan diketahui bahwa cluster 1 memiliki 16 anggota (pelanggan), cluster 2 memiliki 11 anggota dan cluster 3 memiliki 15 anggota. Kata kunci : clustering, metode K-Means, metode K-Medoids, pelanggan, model Fuzzy RFM.

scholarly journals A Max-Min clustering method for $k$-means algorithm of data clustering

2012 ◽  
Vol 8 (3) ◽  
pp. 565-575 ◽  
Author(s):  
Yubo Yuan ◽  
Wanjun Zhang ◽  
Baolan Yuan
Keyword(s):  
Data Clustering ◽  
Clustering Method

scholarly journals FUZZY CLUSTERING MEANS (FCM) DALAM PENENTUAN LOKASI PENERTIBAN PENYAKIT MASYARAKAT PADA KEGIATAN PEMBINAAN SOSIAL SATPOL-PP WILAYAH SUMATRA-BARAT

Petir ◽  
2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Fuzzy Clustering ◽  
Community Policing ◽  
Sex Workers ◽  
Data Clustering ◽  
Behavior Patterns ◽  
Clustering Method ◽  
Street Vendors ◽  
West Sumatra ◽  
Municipal Police ◽  
Disease Community

Based on the data summary disease community policing activities by municipal police pp city of West Sumatra in January 2010 to December 2014, there were as many as 1660 cases of approximately 20 locations enforcement. Each location policing there are various types of activities are classified as a disease of society. Based on data obtained are activities that have curbed such as street vendors, illegal buildings, street children, street, commercial sex workers (CSWs) and others. Number of activities at each point different locations each year, thus requiring data clustering method to facilitate the investigation team in determining the behavior patterns of disease activity as a description of the location community policing a priority next year. The method used in this data clustering method is to use Fuzzy Clustering Means (FCM)

Decoding Behavioral Accuracy in an Attention Task Using Brain fMRI Data

2018 ◽  
Author(s):  
Zhe Wang ◽  
Yu Zheng ◽  
Michael Jigo ◽  
Taosheng Liu ◽  
Jian Ren ◽  
...  
Keyword(s):  
Fmri Data ◽  
Attention Task ◽  
Brain Fmri

Data Clustering Method Using a Modified Gaussian Kernel Metric and Kernel PCA

ETRI Journal ◽  
2014 ◽  
Vol 36 (3) ◽  
pp. 333-342 ◽  
Author(s):  
Hansung Lee ◽  
Jang-Hee Yoo ◽  
Daihee Park
Keyword(s):  
Data Clustering ◽  
Gaussian Kernel ◽  
Kernel Pca ◽  
Clustering Method

On Fuzzy Non-Metric Model for Data with Tolerance and its Application to Incomplete Data Clustering

2016 ◽  
Vol 20 (4) ◽  
pp. 571-579 ◽  
Author(s):  
Yasunori Endo ◽  
◽  
Tomoyuki Suzuki ◽  
Naohiko Kinoshita ◽  
Yukihiro Hamasuna ◽  
...  
Keyword(s):  
Data Clustering ◽  
Incomplete Data ◽  
Clustering Algorithm ◽  
Uncertain Data ◽  
Data Sets ◽  
Membership Degree ◽  
Clustering Methods ◽  
Clustering Method ◽  
Numerical Examples ◽  
Metric Model

The fuzzy non-metric model (FNM) is a representative non-hierarchical clustering method, which is very useful because the belongingness or the membership degree of each datum to each cluster can be calculated directly from the dissimilarities between data and the cluster centers are not used. However, the original FNM cannot handle data with uncertainty. In this study, we refer to the data with uncertainty as “uncertain data,” e.g., incomplete data or data that have errors. Previously, a methods was proposed based on the concept of a tolerance vector for handling uncertain data and some clustering methods were constructed according to this concept, e.g. fuzzyc-means for data with tolerance. These methods can handle uncertain data in the framework of optimization. Thus, in the present study, we apply the concept to FNM. First, we propose a new clustering algorithm based on FNM using the concept of tolerance, which we refer to as the fuzzy non-metric model for data with tolerance. Second, we show that the proposed algorithm can handle incomplete data sets. Third, we verify the effectiveness of the proposed algorithm based on comparisons with conventional methods for incomplete data sets in some numerical examples.

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