scholarly journals Fuzzy C-Means Clustering Model Data Mining For Recognizing Stock Data Sampling Pattern

2007 ◽  
Vol 1 (2) ◽  
Author(s):  
Sylvia Jane Annatje Sumarauw ◽  
Subanar Subanar
Keyword(s):  
Data Mining ◽  
Process Model ◽  
Stock Exchange ◽  
General Meeting ◽  
Data Sampling ◽  
Inference System ◽  
Fuzzy C Means ◽  
Clustering Model ◽  
Fuzzy C Means Clustering ◽  
Sample Pattern

AbstractCapital market has been beneficial to companies and investor. For investors, the capital market provides two economical advantages, namely deviden and capital gain, and a non-economical one that is a voting .} hare in Shareholders General Meeting. But, it can also penalize the share owners. In order to prevent them from the risk, the investors should predict the prospect of their companies. As a consequence of having an abstract commodity, the share quality will be determined by the validity of their company profile information. Any information of stock value fluctuation from Jakarta Stock Exchange can be a useful consideration and a good measurement for data analysis. In the context of preventing the shareholders from the risk, this research focuses on stock data sample category or stock data sample pattern by using Fuzzy c-Me, MS Clustering Model which providing any useful information jar the investors. lite research analyses stock data such as Individual Index, Volume and Amount on Property and Real Estate Emitter Group at Jakarta Stock Exchange from January 1 till December 31 of 204. 'he mining process follows Cross Industry Standard Process model for Data Mining (CRISP,. DM) in the form of circle with these steps: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment. At this modelling process, the Fuzzy c-Means Clustering Model will be applied. Data Mining Fuzzy c-Means Clustering Model can analyze stock data in a big database with many complex variables especially for finding the data sample pattern, and then building Fuzzy Inference System for stimulating inputs to be outputs that based on Fuzzy Logic by recognising the pattern.Keywords: Data Mining, AUz..:y c-Means Clustering Model, Pattern Recognition

scholarly journals Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

2014 ◽  
Vol XL-2/W3 ◽  
pp. 19-23
Author(s):  
P. Akhavan ◽  
M. Karimi ◽  
P. Pahlavani
Keyword(s):  
Data Mining ◽  
Cutaneous Leishmaniasis ◽  
Fuzzy Inference System ◽  
Prevalence Rate ◽  
Fuzzy Systems ◽  
Fuzzy Inference ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering

Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

An Outlier Detection Method Based on Fuzzy C-Means Clustering

2009 ◽  
Vol 419-420 ◽  
pp. 165-168
Author(s):  
Qiang Li ◽  
Jian Pei Zhang ◽  
Guang Sheng Feng
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Detection Method ◽  
Experimental Results ◽  
Data Mining Techniques ◽  
Fuzzy C Means ◽  
Second Stage ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Two Stages

Both fuzzy c-means (FCM) clustering and outlier detection are useful data mining techniques in real applications. In this paper, we show that the task of outlier detection could be achieved as by-product of fuzzy c-means clustering. The proposed strategy consists of two stages. The first stage consists of purely fuzzy c-means process, while the second stage identifies exceptional objects according to a novel metric based on the entropy of membership values. We provide experimental results to demonstrate the effectiveness of our technique.

Analysis and Comparison of Clustering Techniques for Chronic Kidney Disease With Genetic Algorithm

2021 ◽  
pp. 917-927
Author(s):  
Sanat Kumar Sahu ◽  
A. K. Shrivas
Keyword(s):  
Genetic Algorithm ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Hierarchical Clustering ◽  
Adjusted Rand Index ◽  
Feature Selection Technique ◽  
Fuzzy C Means ◽  
Clustering Model ◽  
Fuzzy C Means Clustering ◽  
Evolutionary Technique

The purpose of this article is to weigh up the foremost imperative features of Chronic Kidney Disease (CKD). This study is based mostly on three cluster techniques like; K means, Fuzzy c-means and hierarchical clustering. The authors used evolutionary techniques like genetic algorithms (GA) to extend the performance of the clustering model. The performance of these three clusters: live parameter purity, entropy, and Adjusted Rand Index (ARI) have been contemplated. The best purity is obtained by the K-means clustering technique, 96.50%; whereas, Fuzzy C-means clustering received 93.50% and hierarchical clustering was the lowest at 92. 25%. After using evolutionary technique Genetic Algorithm as Feature selection technique, the best purity is obtained by hierarchical clustering, 97.50%, compared to K –means clustering, 96.75%, and Fuzzy C-means clustering at 94.00%.

Sign in / Sign up

Export Citation Format

Share Document