Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data Warehouse: From Architecture to Implementation
Data Warehouse: From Architecture to Implementation
Knowledge refinement based on the discovery of unexpected patterns in data mining
Decision Support Systems - Special issue: Formal modeling and electronic commerce
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
The new k-windows algorithm for improving the k-means clustering algorithm
Journal of Complexity
Using information retrieval techniques for supporting data mining
Data & Knowledge Engineering
Mining market data: a network approach
Computers and Operations Research
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
Dynamic adaptive ensemble case-based reasoning: application to stock market prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Mining the co-movement in the Taiwan stock funds market
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Interpreting the web-mining results by cognitive map and association rule approach
Information Processing and Management: an International Journal
Mining the hedge and arbitrage of the Taiwan foreign exchange market
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Stock indices prediction using radial basis function neural network
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Hi-index | 12.06 |
One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behavior for investment decisions. The enormous amount of valuable data generated by the stock market has attracted researchers to explore this problem domain using different methodologies. This paper investigates stock market investment issues on Taiwan stock market using a two-stage data mining approach. The first stage Apriori algorithm is a methodology of association rules, which is implemented to mine knowledge and illustrate knowledge patterns and rules in order to propose stock category association and possible stock category investment collections. Then the K-means algorithm is a methodology of cluster analysis implemented to explore the stock cluster in order to mine stock category clusters for investment information. By doing so, this paper proposes several possible Taiwan stock market portfolio alternatives under different circumstances.