Financial Data Mining: Appropriate Selection of Tools, Techniques and Algorithms

2018 ◽  
pp. 244-251 ◽  
Author(s):  
Akash Saxena ◽  
Navneet Sharma ◽  
Khushoo Saxena ◽  
Satyen M. Parikh
Keyword(s):  
Data Mining ◽  
Financial Data ◽  
Selection Of

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.

A Survey of Bayesian Data Mining

Data Mining ◽  
2011 ◽  
pp. 1-26 ◽  
Author(s):  
Stefan Arnborg
Keyword(s):  
Data Mining ◽  
Bayesian Analysis ◽  
Graphical Models ◽  
Bayesian Approach ◽  
Data Sets ◽  
Media Distribution ◽  
Dirichlet Prior ◽  
Approach Problem ◽  
The Bayesian Approach ◽  
Selection Of

This chapter reviews the fundamentals of inference, and gives a motivation for Bayesian analysis. The method is illustrated with dependency tests in data sets with categorical data variables, and the Dirichlet prior distributions. Principles and problems for deriving causality conclusions are reviewed, and illustrated with Simpson’s paradox. The selection of decomposable and directed graphical models illustrates the Bayesian approach. Bayesian and EM classification is shortly described. The material is illustrated on two cases, one in personalization of media distribution, one in schizophrenia research. These cases are illustrations of how to approach problem types that exist in many other application areas.

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.

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