scholarly journals Application of Data Mining Methods in the Design and Creation of New Products and Services

2020 ◽  
Vol 24 (6) ◽  
pp. 14-21
Author(s):  
A. A. Bryzgalov ◽  
E. V. Yaroshenko
Keyword(s):  
Data Mining ◽  
New Products ◽  
Extraction Methods ◽  
Knowledge Extraction ◽  
Self Organizing Map ◽  
Billing System ◽  
Automatic Clustering ◽  
Kohonen Map ◽  
Mining Methods ◽  
Self Organizing

The purpose of research is to substantiate the need to use knowledge extraction methods in the design and creation of new products and services and the feasibility of using the Kohonen self-organizing map method through its formation. Such a map helps to identify previously unknown groups, in particular, as in the case of this article – consumer groups, and their analysis will make it possible to form new tariffs for the services of the mobile operator’s billing system. The main reason for the research is to show organizations the ability to design and create innovative products.Research methods are empirical in nature, based on the collection and accumulation of data on consumer behavior in the market and their subsequent analysis. In order to analyze the collected data, Data Mining methods are used, in particular, the Kohonen self-organizing map method, which allows to obtain automatic clustering of consumers in the market by various characteristics. Clustering was performed using the Kohonen self-organizing map algorithm implemented in the BaseGroup Labs Deductor Studio analytical platform. The choice of this software product is explained by a clear interface and the availability of the required functionality. The study was based on data provided by the mobile operator’s billing system. This is a fairly large amount of data showing the completed operations of mobile operator subscribers.Results. The article provides an overview of sources that offer possible methods for extracting knowledge and ways to process it. The Kohonen map is also built, which allows you to get information about the current situation for mobile subscribers from various independently selected areas. After analyzing this information, the revealed knowledge is applied in the formation of new tariffs and services of the mobile operator. This method of extracting knowledge can also be applied to other large volumes of data from various fields of activity. However, there is a limitation when using this type of knowledge extraction, which is that the data must be structured. If you use unstructured data, you can consider other methods for extracting knowledge described in this article.Conclusion. The article considers the stage of knowledge extraction when designing and creating new products and services based on Data Mining methods, in particular the self-organizing Kohonen map. Innovation in the design and creation of products and services is emphasized by the variability of data in accordance with the dynamic behavior of consumers in the market, which causes the need to periodically review the requirements and concepts of products and services brought to the market.

scholarly journals Implementasi Data Mining untuk mengelompokkan Pasien Rawat Jalan dan Rawat Inap Asuransi Kesehatan

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Ratih
Keyword(s):  
Data Mining ◽  
The Self ◽  
Self Organizing Map ◽  
Clustering Techniques ◽  
Dental Clinics ◽  
Som Algorithm ◽  
Outpatient Health Services ◽  
Internal Medicine Clinic ◽  
Eye Clinic ◽  
Self Organizing

Patient Visits Outpatient and inpatient insurance at Class C Hospitals is increasing from year to year. Increased visits to insurance patients will have an impact on the inpatient and outpatient health services provided. From the increase in patient visits, the data owned by the hospital is increasingly abundant. The data can be used to explore knowledge, find certain patterns. To explore knowledge about Inpatient and Outpatient Insurance patients, data mining clustering techniques are used with the Self Organizing Map (SOM) algorithm using R Studio tools. Clustering technique with the implementation of the Self Organizing Map (SOM) algorithm is a technique for grouping data based on certain characteristics which are then mapped into areas that resemble map shapes. The CRISP-DM method is used in this study to perform the stages of the data mining process. The results obtained from the implementation of clustering with the Self Organizing Map (SOM) algorithm are obtained 2 clusters representing dense areas and non-congested areas. Dense areas are represented by Internal Medicine Clinic, Surgery Clinic, Eye Clinic, Hemodialysis, Melati Room, Orchid Room, Bougenville Room, Flamboyan Room. Non-crowded areas are represented by General Clinics, Dental Clinics, Obstetrics and Gynecology Clinics, Children's Clinics, Mawar Room and Soka Room

scholarly journals Analisis Cluster Data Interkomparasi Anak Timbangan dengan Algoritma Self Organizing Maps

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Arif Fajar Solikin ◽  
Kusrini Kusrini ◽  
Ferry Wahyu Wibowo
Keyword(s):  
Data Mining ◽  
Statistical Test ◽  
Optimum Number ◽  
Data Normalization ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Number Of Clusters ◽  
Map Algorithm ◽  
Cluster Data ◽  
Self Organizing

Intercomparison was conducted to determine the ability and the performance of the laboratory. Intercomparison results are usually expressed in the range of En ratio values (En ?|1|) which express the equivalence of one laboratory with other laboratories. If the laboratory is declared unequal, then it needs to identify the source of the problem by itself. To make it easier, it can be done by Clustering which is one of the data mining techniques. Clustering is done by applying a self organizing map algorithm on the KNIME (Konstanz Information Miner) analytic tools. Several experiments were carried out with different layer size and data normalization status from one experiment to another experiment. The results were analyzed through pseudo F statistical test and icdrate test. The largest pseudo F statistic value was obtained from the 8th experiment (setting the layer size 2x2 without data normalization) with a pseudo F statistic value of 167.53 for 1kg artifacts and a Pseudo F statistic value of 104.86 for 200 g artifacts where the optimum number of clusters are 4. The smallest icdrate value was obtained from the 5th experiment (setting the 2x3 layer size without data normalization) with an icdrate value of 0.0713 for 1kg artifacts and icdrate value of 0.2889 for 200g artifacts with the best number of clusters being 6. From 12 laboratories can be grouped into 6 groups where each group has the same identification. There are groups 1, 3 and 6 have 1 member, while groups 2, 4 and 5 have 3 members.

scholarly journals CLUSTERING TRAFO DISTRIBUSI MENGGUNAKAN ALGORITMA SELF-ORGANIZING MAP

2017 ◽  
Vol 8 (1) ◽  
pp. 15-20
Author(s):  
Tutik Khotimah ◽  
Abdul Syukur ◽  
M. Arief Soeleman
Keyword(s):  
Data Mining ◽  
Self Organizing Map ◽  
Self Organizing

Salah satu cara untuk mengetahui beban sebuah trafo distribusi PLN masih memenuhi batas normal atau overload adalah dengan melakukan pengukuran beban trafo tersebut. Pada PLN Area Pelayanan Jaringan Kudus, pengukuran beban dilakukan baik pada siang hari mau pun pada malam hari. Hasil pengukuran tersebut memiliki kemungkinan berbeda. Hal ini disebabkan pada siang hari penggunaan beban cenderung kecil, sedangkan pada malam hari pemakaian beban lebih besar. Hal ini menyebabkan sulitnya menentukan beban trafo tersebut masih normal atau overload. Untuk memetakan beban trafo distribusi secara cepat dan akurat, diperlukan teknik data mining yaitu clustering. Penelitian ini dilakukan dengan menerapkan algoritma Self Organizing Map (SOM). Dengan SOM dihasilkan nilai akurasi sebesar 93% terhadap hasil pengukuran beban trafo distribusi pada siang hari dan sebesar 84% terhadap hasil pengukuran beban trafo distribusi pada malam hari. Sedangkan error yang dihasilkan dari pemetaan dengan SOM sebesar 7% terhadap hasil pengukuran beban trafo distribusi pada siang hari dan sebesar 16% terhadap hasil pengukuran beban trafo distribusi pada malam hari.

scholarly journals Neural networks in data mining

2012 ◽  
Vol 49 (No. 9) ◽  
pp. 427-431 ◽  
Author(s):  
AVeselý
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Knowledge Engineering ◽  
Data Sets ◽  
Data Mining Algorithm ◽  
Self Organizing Map ◽  
New Methods ◽  
Area Of Interest ◽  
Database Theory ◽  
Self Organizing

To posses relevant information is an inevitable condition for successful enterprising in modern business. Information could be parted to data and knowledge. How to gather, store and retrieve data is studied in database theory. In the knowledge engineering, there is in the centre of interest the knowledge and methods of its formalization and gaining are studied. Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining. Classical methods of gaining knowledge from data sets are statistical methods. In data mining, new methods besides statistical are used. These new methods have their origin in artificial intelligence. They look for unknown and unexpected relations, which can be uncovered by exploring of data in database. In the article, a utilization of modern methods of data mining is described and especially the methods based on neural networks theory are pursued. The advantages and drawbacks of applications of multiplayer feed forward neural networks and Kohonen’s self-organizing maps are discussed. Kohonen’s self-organizing map is the most promising neural data-mining algorithm regarding its capability to visualize high-dimensional data.

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