Data mining is to discover and assess significant patterns from data, followed by the validation of these identified patterns. Data mining is the process to evaluate the data from different perceptions and summarizing it into valuable information. This summarized information consequently can be used to design business strategies to upsurge revenue, occasionally drive down costs, or both. The Apriori association algorithm is based on pre-computed frequent item sets and it has to scan the entire transaction log / dataset or database which will become a problem with large item sets. With FP trees, there is no necessity for candidate generation, unlike in the Apriori algorithm, and the frequently occurring item sets are discovered by just traversing the FP tree. This paper discusses the FP Tree concept and implements it using Java for a general social survey dataset. We use this approach to determine association rules that occur in the dataset. In this manner, we can establish relevant rules and patterns in any set of records.
The ultimate goal of data mining is prediction-and predictive data mining is the most common type of data mining and one that has most direct business applications. This paper discusses how one can apply data mining to design a market capital prediction system for trading firms [1]. The dataset is normalized and trained. The paper delves into the field of neural networks and shows how it can be utilized, in combination with the Graphical user Interface of MATLAB, GUIDE, to make accurate predictions. When implemented, the trained system can be used to forecast the market capital for a particular combination of input parameters. The accuracy of this method demonstrates its utility as a predictive tool.
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