scholarly journals Implementasi Metode Clustering DBSCAN pada Proses Pengambilan Keputusan

2015 ◽  
pp. 185
Author(s):  
Anindya Santika Devi ◽  
I Ketut Gede Darma Putra ◽  
I Made Sukarsa
Keyword(s):  
Decision Making ◽  
Spatial Data ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Decision Making Process ◽  
Number Of Clusters ◽  
Potential Customer ◽  
A Company ◽  
Support Decision Making

Spatial Data Clustering is one of the significant techniques in data mining which used to obtain information or knowledge in a large number of spatial data from various applications. One technique that being a pioneer in the development of spatial data clustering algorithm is DBSCAN. This study is focused on implementation of DBSCAN method in decision making process in order to help a company to decide its potential customer. The trial results in this study show that DBSCAN method has been successfully conduct clustering process to support decision making process in determination of potential customer by forming several number of clusters.

scholarly journals Behavioural approach to defining corporate objectives

2017 ◽  
Vol 45 (4) ◽  
pp. 55-67
Author(s):  
Joanna Błach ◽  
Maria Gorczyńska
Keyword(s):  
Decision Making ◽  
Decision Making Process ◽  
Behavioural Approach ◽  
Behavioural Model ◽  
Decision Making Model ◽  
A Company

The main aim of this paper is to present the behavioural decision-making model as an alternative to the neoclassical approach to defining corporate objectives. Elementary stag-es of the behavioural decision-making process are presented with regard to the interests of various stakeholders. The analysis of the practical solutions revealed that the majority of the analysed companies emphasize a behavioural approach to defining objectives, while using a neoclassical approach. In this way the contents of the defined objectives become complementary. It was also found that limited interest in a behavioural model of deci-sion-making may result from the unclear principles regarding the determination of aspira-tion levels and the difficulty in assessing the performance of a company.

scholarly journals Sistem Pendukung Keputusan Penentuan Lokasi Industri Berbasis Spasial Menggunakan Metode MOORA

2020 ◽  
Vol 7 (3) ◽  
pp. 639-646
Author(s):  
Agusta Praba Ristadi Pinem ◽  
Henny Indriyawati ◽  
Basworo Ardi Pramono
Keyword(s):  
Decision Making ◽  
Spatial Data ◽  
System Modeling ◽  
Decision Making Process ◽  
Industry Location ◽  
Location Determination ◽  
Strategic Location ◽  
The Right ◽  
Moora Method

Information technology is developing rapidly and the effect is every single organization will always collect data and information. The information collected is used as a basis for decision making. But not all information can be directly used for the decision making process. Method and weighting are needed in the process of getting information. One method that can be used to support the decision making process is Multi-Objective Optimization on the basis of Ratio Analysis (MOORA). MOORA is included in the Multi Criteria Decision Making (MCDM) which makes it possible to provide the best choice of information from several choices by using criteria values. This research uses the MOORA method as determining strategic industrial locations by combining spatial data. In determining the strategic location of the industry, MOORA uses several criteria and different weights for each criteria. The MOORA with spatial data can be produce the right information related to the determination of strategic industry locations by finding the correlation between method results with industry location in Semarang city. The results obtained from this research are the formation of a decision support system modeling of industrial location determination using the MOORA method with spatial data. Correlation value generated by the Spearman Rank method is 0,9.

scholarly journals A Comparative Study between of Fuzzy C-Means Algorithms and Density based Spatial Clustering of Applications with Noise

2018 ◽  
Vol 7 (3.33) ◽  
pp. 131
Author(s):  
Kwang Kyu Lee ◽  
. .
Keyword(s):  
Decision Making ◽  
Spatial Data ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Data Sets ◽  
Fuzzy C Means ◽  
Autonomous Decision ◽  
Density Based Clustering ◽  
New Knowledge

Data mining technology has emerged as a means of identifying patterns and trends from large amounts of data and is a computing intelligence area that provides tools for data analysis, new knowledge discovery, and autonomous decision making. Data clustering is an important problem in many areas. Fuzzy C-Means(FCM)[11,12,13] is a very important clustering technique based on fuzzy logic. DBSCAN(Density Based Spatial Clustering of Applications with Noise)[8] is a density-based clustering algorithm that is suitable for dealing with spatial data including noise and is a collection of arbitrary shapes and sizes. In this paper, we compare and analyze the performance of Fuzzy C-Means and DBSCAN algorithms in different data sets.  

scholarly journals Determination of Industrial Location Using the WASPAS Method with Spatial Data as Criteria Data

2020 ◽  
Vol 4 (4) ◽  
pp. 691-696
Author(s):  
Agusta Praba Ristadi Pinem ◽  
Siti Asmiatun ◽  
Astrid Novita Putri
Keyword(s):  
Decision Making ◽  
Spatial Data ◽  
Site Selection ◽  
System Model ◽  
Decision Making Process ◽  
Multi Criteria Decision Making ◽  
Precise Information ◽  
Product Assessment ◽  
The City

Today, the development of the use of spatial data is not only used for information geographic or transportation. But also can be used for site selection with integrating decision support system methods. Generated information can help in making decisions and meet the expected aspects. One method that can be used to support the decision making process is the Weighted Aggregated Sum Product Assessment (WASPAS). WASPAS is included in Multi Criteria Decision Making which can produce selected information from the data or criteria used. This study uses the WASPAS method as a determinant of strategic industrial locations by spatial data collection. In determining strategic industrial locations, WASPAS uses several different criteria and weights for each criterion. The WASPAS method can produce precise information related to the determination of strategic industrial locations. The results of the Spearman Rating trial with data on industrial locations in the city of Semarang show a strong conformity, as seen from the resulting compatibility value of 1.0. The results obtained from this study are the establishment of a system model that supports the decision to determine the location of the industry using the WASPAS method.

scholarly journals A New Grey Approach for Using SWARA and PIPRECIA Methods in a Group Decision-Making Environment

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1554
Author(s):  
Dragiša Stanujkić ◽  
Darjan Karabašević ◽  
Gabrijela Popović ◽  
Predrag S. Stanimirović ◽  
Muzafer Saračević ◽  
...  
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Multiple Criteria Decision Making ◽  
Grey System Theory ◽  
Multiple Criteria ◽  
Decision Making Process ◽  
Grey System ◽  
Criteria Weights

The environment in which the decision-making process takes place is often characterized by uncertainty and vagueness and, because of that, sometimes it is very hard to express the criteria weights with crisp numbers. Therefore, the application of the Grey System Theory, i.e., grey numbers, in this case, is very convenient when it comes to determination of the criteria weights with partially known information. Besides, the criteria weights have a significant role in the multiple criteria decision-making process. Many ordinary multiple criteria decision-making methods are adapted for using grey numbers, and this is the case in this article as well. A new grey extension of the certain multiple criteria decision-making methods for the determination of the criteria weights is proposed. Therefore, the article aims to propose a new extension of the Step-wise Weight Assessment Ratio Analysis (SWARA) and PIvot Pairwise Relative Criteria Importance Assessment (PIPRECIA) methods adapted for group decision-making. In the proposed approach, attitudes of decision-makers are transformed into grey group attitudes, which allows taking advantage of the benefit that grey numbers provide over crisp numbers. The main advantage of the proposed approach in relation to the use of crisp numbers is the ability to conduct different analyses, i.e., considering different scenarios, such as pessimistic, optimistic, and so on. By varying the value of the whitening coefficient, different weights of the criteria can be obtained, and it should be emphasized that this approach gives the same weights as in the case of crisp numbers when the whitening coefficient has a value of 0.5. In addition, in this approach, the grey number was formed based on the median value of collected responses because it better maintains the deviation from the normal distribution of the collected responses. The application of the proposed approach was considered through two numerical illustrations, based on which appropriate conclusions were drawn.

Tree-ART2 Learning Model for Spatial Clustering in Second Dimension

2014 ◽  
Vol 543-547 ◽  
pp. 1934-1938
Author(s):  
Ming Xiao
Keyword(s):  
Network Model ◽  
Spatial Data ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Adaptive Resonance Theory ◽  
Spatial Distance ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Vector Module

For a clustering algorithm in two-dimension spatial data, the Adaptive Resonance Theory exists not only the shortcomings of pattern drift and vector module of information missing, but also difficultly adapts to spatial data clustering which is irregular distribution. A Tree-ART2 network model was proposed based on the above situation. It retains the memory of old model which maintains the constraint of spatial distance by learning and adjusting LTM pattern and amplitude information of vector. Meanwhile, introducing tree structure to the model can reduce the subjective requirement of vigilance parameter and decrease the occurrence of pattern mixing. It is showed that TART2 network has higher plasticity and adaptability through compared experiments.

scholarly journals Finding the Number of Clusters in Data and Better Initial Centers for K-means Algorithm

2020 ◽  
Vol 12 (6) ◽  
pp. 1-20
Author(s):  
Ahmed Fahim ◽  
Keyword(s):  
Data Clustering ◽  
Linear Time ◽  
Original Data ◽  
Local Minima ◽  
Expected Number ◽  
Open Problems ◽  
Number Of Clusters ◽  
Benchmark Datasets ◽  
Selection Of

The k-means is the most well-known algorithm for data clustering in data mining. Its simplicity and speed of convergence to local minima are the most important advantages of it, in addition to its linear time complexity. The most important open problems in this algorithm are the selection of initial centers and the determination of the exact number of clusters in advance. This paper proposes a solution for these two problems together; by adding a preprocess step to get the expected number of clusters in data and better initial centers. There are many researches to solve each of these problems separately, but there is no research to solve both problems together. The preprocess step requires o(n log n); where n is size of the dataset. This preprocess step aims to get initial portioning of data without determining the number of clusters in advance, then computes the means of initial clusters. After that we apply k-means on original data using the resulting information from the preprocess step to get the final clusters. We use many benchmark datasets to test the proposed method. The experimental results show the efficiency of the proposed method.

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