scholarly journals Implementasi Algoritma DBScan dalam Pemngambilan Data Menggunakan Scatterplot

2021 ◽  
Vol 6 (2) ◽  
pp. 91-98
Author(s):  
Wawan Gunawan
Keyword(s):  
Data Mining ◽  
Silhouette Index ◽  
Minimum Points

Seiring dengan perkembangan teknologi informasi dan komunikasi, semakin banyak data yang digunakan dalam suatu pemecahan masalah. Tetapi, dengan banyaknya data yang ada sangat sulit mencari informasi yang diinginkan. Oleh karena itu, dilakukan data mining untuk mengekstraksi pengetahuan secara otomatis dari data berukuran besar dengan cara mencari pola-pola menarik yang terkandung di dalam data tersebut. Dalam penelitian ini, peneliti menggunakan algoritma DBSCAN dalam penelitiannya. Data yang digunakan adalah data spasial mahasiswa Universitas Mercu Buana. Dari data ini, peneliti mengambil informasi scatterplot yang terbentuk, lalu dengan algoritma DBSCAN untuk melihat cluster yang terbentuk, dan melakukan validasi dengan Silhouette Index. Dari penelitian ini dapat disimpulkan bahwa algoritma DBSCAN berhasil diimplementasikan pada data mahasiswa Universitas Mercu Buana. Dan hasil pengujian dari implementasi algoritma DBSCAN dipengaruhi oleh dua nilai parameter yaitu Minimum Points, dan Epsilon.

scholarly journals VISUALIZATION MODULE OF DENSITY-BASED CLUSTERING FOR HOTSPOT DISTRIBUTION IN INDONESIA USING MAPSERVER

2016 ◽  
Vol 7 (3) ◽  
pp. S58-S60
Author(s):  
Yenni Puspitasari ◽  
Imas Sukaesih Sitanggang ◽  
Rina Trisminingsih
Keyword(s):  
Data Mining ◽  
Information System ◽  
Geographic Information System ◽  
Geographic Information ◽  
Mining Method ◽  
Hotspot Analysis ◽  
Web Based ◽  
Dbscan Algorithm ◽  
Density Based Clustering ◽  
Minimum Points

A web-based Geographic Information System (GIS) has been built by previous researchers to visualize hotspots data in Indonesia. That GIS still has not contained a hotspot analysis module. Data mining method can be used to analyze hotspot data. This research aims to develop and to integrate a clustering module of hotspot in GIS which has been developed in the previous research. The clustering module for grouping hotspot data was built using the DBSCAN algorithm with PHP programming language. Clustering hotspot data was performed based on year, month, and province. Clustering parameters are epsilon and minimum points (MinPts). Epsilon value that used ranged from 0.01 to 0.1 while MinPts ranges from 1 to 6. The clustering results are shown in form of table which consists of the attribute Province, Regency, Latitude, Longitude and Cluster. Cluster column is the final result of clustering using algorithm DBSCAN. The attribute cluster represents clusters are visualized using the map of Indonesia that was built using MapServer. Visualization can help parties involved in making effective and efficient decisions to prevent forest fires.Key words: clustering, DBSCAN algorithm, geographic information system, hotspot

scholarly journals Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods

2020 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Rahma Wati Sembiring Brahmana ◽  
Fahd Agodzo Mohammed ◽  
Kankamol Chairuang
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Customer Segmentation ◽  
Customer Relationship ◽  
Cluster Center ◽  
Cluster Group ◽  
Rfm Model ◽  
Silhouette Index ◽  
Marketing Activities ◽  
Potential Customers

A problem that appears in marketing activities is how to identify potential customers. Marketing activities could identify their best customer through customer segmentation by applying the concept of Data Mining and Customer Relationship Management (CRM). This paper presents the Data Mining process by combining the RFM model with K-Means, K-Medoids, and DBSCAN algorithms. This paper analyzes 334,641 transaction data and converts them to 1661 Recency, Frequency, and Monetary (RFM) data lines to identify potential customers. The K-Means, K-Medoids, and DBSCAN algorithms are very sensitive for initializing the cluster center because it is done randomly. Clustering is done by using two to six clusters. The trial process in the K-Means and K-Medoids Method is done using random centroid values ??and at DBSCAN is done using random Epsilon and Min Points, so that a cluster group is obtained that produces potential customers. Cluster validation completes using the Davies-Bouldin Index and Silhouette Index methods. The result showed that K-Means had the best level of validity than K-Medoids and DBSCAN, where the Davies-Bouldin Index yield was 0,33009058, and the Silhouette Index yield was 0,912671056. The best number of clusters produced using the Davies Bouldin Index and Silhouette Index are 2 clusters, where each K-Means, K-Medoids, and DBSCAN algorithms provide the Dormant and Golden customer classes.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

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