Data mining using integration of clustering and decision tree algorithm has been proposed for predicting the stock market prices. This mechanism involves studying stock price patterns in time by attempting to predict future results of a time-series by simply studying patterns in the time-series of stock prices. The goal of this project is to implement data mining in order to predict the Time-Se...

The daily stock turning point detection problems are investigated in this study. The Support Vector Regression model has been applied in various forecasting applications and proved to be with stable performances. In this research, SVR has been used to predict the trading signal since it could handle overall information effectively even under the complex environment of stock price variations. Th...

The problem of mining association rules in a database are introduced. Most of association rule mining approaches aim to mine association rules considering exact matches between items in transactions. A new algorithm called ―Without expert fuzzy based data mining Based on Fuzzy Similarity to mine new Association Rules ‖ which considers not only exact matches between items, but also the fuzzy sim...

SPECIAL ISSUE PAPERS WS-DAI-DM: An Interface Specification for Data Mining in Grid Environments Yan Zhang, Luoming Meng, Honghui Li, Alexander Woehrer, and Peter Brezany An Ensemble Data Mining and FLANN Combining Short-term Load Forecasting System for Abnormal Days Ming Li and Junli Gao Incremental Learning for Dynamic Collaborative Filtering Shuli Han, Yujiu Yang, and Wenhuang Liu Symbolic Re...

This paper proposes a new dual factor time-invariant fuzzy time series method that is capable of forecasting stock market Price Index. The proposed approach uses a new fuzzy logic relationship definition. According to the utilized membership degrees used to define the fuzzy relationships, each datum may belong to two distinct intervals rather than only one interval. This assumption, which has n...

Data mining is an iterative and interactive process concerned with discovering patterns, associations and periodicity in real world data. This chapter presents two real world applications where evolutionary computation has been used to solve network management problems. First, we investigate the suitability of linear genetic programming (LGP) technique to model fast and efficient intrusion dete...

This study proposes a modified version of cultural algorithms (CAs) which benefits from rule-based system for influence function. This rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. This is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. This rule ...

Complex networks and networked data mining p. 10 In-depth data mining and its application in stock market p. 13 Relevance of counting in data mining tasks p. 14 Term graph model for text classification p. 19 A latent usage approach for clustering Web transaction and building user profile p. 31 Mining quantitative association rules on overlapped intervals p. 43 An approach to mining local causal...

Traditional forecasting methods need strict assumptions such as normality and linearity. It is very difficult to satisfy these assumptions for real-world time series. Many realworld time series can be easily analyzed by using fuzzy time series methods since fuzzy time series methods do not require any strict assumptions. Therefore, fuzzy time series approaches have been getting more and more at...

One of the most important methods of opacity accounting information by management is to accelerate the identification of good news versus delaying the identification of bad news on profits, but there is always a final level of accumulation of bad news in the company, and by reaching that its final level, these bad news will be released, which will lead to a Stock Price Crash Risk. In fact, stoc...