چکیده مقاله
Time series data poses a significant variation to the traditional segmentation techniques of data miningbecause the observation is derived from multiple instances of the same underlying record In this paperwe propose a new pattern for extracting knowledge form stock market by eliminating some partialfluctuation and using clustering algorithm of data mining which bring us efficient information aboutcurrent situation Since similarity measurements of time series play a crucial role in many KDDapplications, data mining clustering techniques could be used in extracting hidden information at equaltime intervals Evaluating and analyzing the results help us; find efficient reasons on stock marketingportfolios variation
کلیدواژهها
نویسندگان
Ali Ilkhani
Department of Knowledge Management Tehran Securities Exchange Technology Management Co, Tehran, Iran
Golnoosh Abaee
Department of Computer Science Islamic Azad University Roodehen Branch
شیوه ارجاع
Ilkhani, Ali and Abaee, Golnoosh,1389,KNOWLEDGE DISCOVERY FROM IRANIAN CAPITAL MARKET BY USING DATA PATTERN CLUSTERING,5th international conference on e-commerce in developing countries with focus on export,Kish Island
ارائهشده در
مجموعه مقالات هفتمین کنفرانس بین المللی تجارت الکترونیک در کشورهای در حال توسعه با رویکرد بر امنیت ECDC201328 فروردین 1392 · جزیره کیش