AIS Data Pre-Processing for Trajectory Clustering Data Preparation

2021 ◽  
Author(s):  
I Putu Noven Hartawan ◽  
I Made Oka Widyantara ◽  
A. A. I. N. E. Karyawati ◽  
Ngurah Indra Er ◽  
Ketut Buda Artana ◽  
...  
Keyword(s):  
Data Preparation ◽  
Trajectory Clustering ◽  
Clustering Data

Penerapan Data Mining Untuk Prediksi Penjualan Mobil Menggunakan Metode K-Means Clustering

2020 ◽  
Vol 3 (3) ◽  
pp. 187-201
Author(s):  
Sufajar Butsianto ◽  
Nindi Tya Mayangwulan
Keyword(s):  
Data Mining ◽  
Clustering Data ◽  
Cluster 2

Penggunaan mobil di Indonesia setiap tahunnya selalu meningkat dan membuat perusahaan otomotif berlomba-lomba dalam peningkatan penjualannya. Tujuan dari penelitian ini untuk mengelompokan data penjualan kedalam sebuah cluster dengan metode Data Mining Algoritma K-Means Clustering. Data Penjualan nantinya akan dikelompokan berdasarkan kemiripan data tersebut sehingga data dengan karakteristik yang sama akan berada dalam satu cluster. Atribut yang digunakan adalah brand dan penjualan. Cluster yang terbentuk setelah dilakukan proses K-Means Clustering terbagi menjadi tiga cluster yaitu Cluster 0 jumlah anggota 235 dengan presentase 26% dikategorikan Laris, Cluster 1 jumlah anggota 604 dengan presentase 67% dikategorikan Kurang Laris, dan Cluster 2 jumlah angota 61 dengan presentase 7% dikategorikan Paling Laris, dari proses clustering diatas dapat diperoleh validasi DBI (Davies Bouldin Index) dengan nilai 0,341

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

IMPLEMENTASI DATA MINING UNTUK MENENTUKAN TINGKAT PENJUALAN PAKET DATA TELKOMSEL MENGGUNAKAN METODE K-MEANS CLUSTERING

2020 ◽  
Vol 25 (1) ◽  
pp. 76-88
Author(s):  
Suhandio Handoko ◽  
Fauziah Fauziah ◽  
Endah Tri Esti Handayani
Keyword(s):  
Data Mining ◽  
Clustering Data

Perkembangan industri telekomunikasi saat ini sangat pesat karena telekomunikasi sudah menjadi kebutuhan utama bagi masyarakat sehingga banyak perusahaan yang bergerak di industry telekomunikasi. Banyaknya industry Telekomunikasi menuntut para pengembang untuk menemukan strategi atau suatu pola yang dapat meningkatkan penjualan dan pemasaran produk, salah satu strateginya adalah dengan memanfaatkan data transaksi. Paket data merupakan produk dibidang telekomunikasi. Proses Clustering saat ini masih di lakukan secara manual sehingga membutuhkan waktu, proses perhitungan dan ketelitian yang tinggi. Pada penelitian ini dibuat aplikasi berbasis website dengan tujuan untuk mempermudah Clustering data sehingga dapat digunakan sebagai referensi dalam perencanaan promosi produk telkomsel ke berbagai daerah. Metode yang digunakan untuk mengatasi permasalahan tersebut yaitu metode Clustering dengan menggunakan Algoritma K-Means. Algoritma K-Means merupakan algoritma pengelompokkan sejumlah data menjadi menjadi kelompok-kelompok data tertentu. Pada penelitian ini data penjualan dikelompokkan menjadi 3 yaitu data penjualan rendah, data penjualan sedang dan data penjualan tinggi. Pengujian clustering dengan algoritma K-Means pada aplikasi terhadap data transaksi penjualan paket telkomsel diperoleh persentase kesesuaian yaitu 100% dibandingkan dengan clustering manual.

Vehicle trajectory clustering based on 3D information via a coarse-to-fine strategy

2017 ◽  
Vol 22 (5) ◽  
pp. 1433-1444 ◽  
Author(s):  
Huansheng Song ◽  
Xuan Wang ◽  
Cui Hua ◽  
Weixing Wang ◽  
Qi Guan ◽  
...  
Keyword(s):  
Trajectory Clustering ◽  
Vehicle Trajectory ◽  
3D Information ◽  
Coarse To Fine

scholarly journals A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 786
Author(s):  
Yenny Villuendas-Rey ◽  
Eley Barroso-Cubas ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Swarm Intelligence ◽  
Data Clustering ◽  
Incomplete Data ◽  
Missing Values ◽  
Clustering Algorithms ◽  
Bat Algorithm ◽  
Hybrid Features ◽  
Bee Colony ◽  
Learning Tasks ◽  
Clustering Data

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.

scholarly journals Review on additive hybrid- and multi-material-manufacturing of metals by powder bed fusion: state of technology and development potential

2021 ◽  
Author(s):  
M. Schneck ◽  
M. Horn ◽  
M. Schmitt ◽  
C. Seidel ◽  
G. Schlick ◽  
...  
Keyword(s):  
Review Paper ◽  
Powder Bed Fusion ◽  
Process Chain ◽  
Data Preparation ◽  
Powder Bed ◽  
Theoretical Approaches ◽  
Powder Deposition ◽  
Readiness Level ◽  
Manufacturing Readiness ◽  
2D And 3D

AbstractIn this review paper, the authors investigate the state of technology for hybrid- and multi-material (MM) manufacturing of metals utilizing additive manufacturing, in particular powder bed fusion processes. The study consists of three parts, covering the material combinations, the MM deposition devices, and the implications in the process chain. The material analysis is clustered into 2D- and 3D-MM approaches. Based on the reviewed literature, the most utilized material combination is steel-copper, followed by fusing dissimilar steels. Second, the MM deposition devices are categorized into holohedral, nozzle-based as well as masked deposition concepts, and compared in terms of powder deposition rate, resolution, and manufacturing readiness level (MRL). As a third aspect, the implications in the process chain are investigated. Therefore, the design of MM parts and the data preparation for the production process are analyzed. Moreover, aspects for the reuse of powder and finalization of MM parts are discussed. Considering the design of MM parts, there are theoretical approaches, but specific parameter studies or use cases are not present in the literature. Principles for powder separation are identified for exemplary material combinations, but results for further finalization steps of MM parts have not been found. In conclusion, 3D-MM manufacturing has a MRL of 4–5, which indicates that the technology can be produced in a laboratory environment. According to this maturity, several aspects for serial MM parts need to be developed, but the potential of the technology has been demonstrated. Thus, the next important step is to identify lead applications, which benefit from MM manufacturing and hence foster the industrialization of these processes.

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