Fuzzy C-Means Clustering Model Data Mining For Recognizing Stock Data Sampling Pattern - Sylvia Jane Annatje Sumarauw; Subanar Subanar (IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., 2007)
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