Factor identification for insurance pricing mechanism using data mining and multi criteria decision making

2021 ◽  
Author(s):  
Mobina Mousapour Mamoudan ◽  
Danial Forouzanfar ◽  
Zahra Mohammadnazari ◽  
Amir Aghsami ◽  
Fariborz Jolai
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Multi Criteria Decision Making ◽  
Insurance Pricing ◽  
Pricing Mechanism ◽  
Using Data

A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran

2021 ◽  
Vol 80 (15) ◽  
Author(s):  
Elham Rafiei Sardooi ◽  
Ali Azareh ◽  
Tayyebeh Mesbahzadeh ◽  
Farshad Soleimani Sardoo ◽  
Eric J. R. Parteli ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Hybrid Model ◽  
Risk Mapping ◽  
Landslide Risk ◽  
Multi Criteria Decision Making ◽  
Golestan Province ◽  
Using Data

scholarly journals Application of Consumer Clustering Mining Data Mining in Household with Fuzzy Multi Criteria Decision Making (FMCDM)

2017 ◽  
Vol 2 (1) ◽  
pp. 31
Author(s):  
Muhammad Anshari ◽  
I Putu Suryadharma ◽  
Nyoman Putra Sastra
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Multi Criteria Decision Making ◽  
Environmental Criteria ◽  
Selection Of ◽  
Help System

This study aims to classify consumers in the selection of houses using Fuzzy Multi Criteria Decision Making (FMCDM) method based on data mining. Alternative houses provided there are four of the minimalist houses, contemporary modern homes, classical houses, and traditional ethnic houses. To generate these choices, there are five criteria: price criteria, home/type criteria, interior criteria, exterior criteria, and home environmental criteria. The results of this study can help system users in determining the choice of home type based on the user's tastes of the criteria available and also can help the investors and contractors in building houses, villas, hotels, and housing of the criteria.

scholarly journals Retail Giant Sales Forecasting using Machine Learning

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2408-2411
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Learning Model ◽  
Accurate Prediction ◽  
Machine Learning Model ◽  
Different Types ◽  
Using Data ◽  
Near Future ◽  
Sales Trend

Sales forecasting is widely recognized and plays a major role in an organization’s decision making. It is an integral part in business execution of retail giants, so that they can change their strategy to improve sales in the near future. This helps in better management of their resources like machine, money and manpower. Forecasting the sales will help in managing the revenue and inventory accordingly. This paper proposes a model that can forecast most profitable segments at granular level. As most retail giants have many branches in different locations, consolidation of sales are hard using data mining. Instead using machine learning model helps in getting reliable and accurate results. This paper helps in understanding the sales trend to monitor or predict future applicable on different types of sales patterns and products to produce accurate prediction results.

scholarly journals Analisis Pola Pembelian Obat di Apotek Sekar Adi Menggunakan Metode Algoritma Apriori Depok

2020 ◽  
Vol 4 (1) ◽  
pp. 112
Author(s):  
Siti Awaliyah Rachmah Sutomo ◽  
Frisma Handayanna
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Association Rules ◽  
High Frequency ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Mining Methods ◽  
Using Data ◽  
Purchase Patterns

By using data mining methods can be processed to obtain information and assist in decision making, the amount of data on sales transactions in each drug purchase can cause a data accumulation and various problems, such as drug stock inventory, and sales transaction data, with Data mining techniques, the behavior of consumers in making transactions of drug purchase patterns can be analyzed, It can be known what drugs are commonly purchased by mostly people, the application of Apriori Algorithm is expected to help in forming a combination of itemset. The process of determining drug purchase patterns can be carried out by applying the Appriori algorithm method, determination of drug purchase patterns can be done by looking at the results of the consumer's tendency to buy drugs based on a combination of 3 itemset. By calculating the Analysis of High Frequency Patterns and the Formation of Association Rules, with a minimum of 30% support, there is a combination of 3 itemsset namely MOLAGIT PER TAB (M1), VIT C TABLET (V2), and PARACETAMOL 500 MG TABLET (P2) with 33.33 % support results obtained, and with minimum confidence of 65% there are 6 final association rules.

Using Data Mining Techniques to Discover Patterns in an Airline's Flight Hours Assignments

2021 ◽  
pp. 107-126
Author(s):  
Francisco Javier Villar Martín ◽  
Jose Luis Castillo Sequera ◽  
Miguel Angel Navarro Huerga
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information System ◽  
Predictive Modeling ◽  
Data Model ◽  
Free Software ◽  
Data Mining Techniques ◽  
Visualization Tools ◽  
Using Data

The quality of a company's information system is essential and also its physical data model. In this article, the authors apply data mining techniques in order to generate knowledge from the information system's data model, and also to discover and understand hidden patterns within data that regulate the planning of flight hours of pilots and copilots in an airline. With this objective, they use Weka free software which offers a set of algorithms and visualization tools geared to data analysis and predictive modeling of information systems. Firstly, they apply clustering to study the information system and analyze data model; secondly, they apply association rules to discover connection patterns in data; and finally, they generate a decision tree to classify and extract more specific patterns. The authors suggest conclusions according these information system's data to improve future decision making in an airline's flight hours assignments.

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