Weighted FP-Tree Mining Algorithms for Conversion Time Data Flow

다국어 초록 (Multilingual Abstract)

The data distribution in the data streams usually changes dynamically with time. Traditional mining algorithms based on transaction are difficult to establish the correlation between time characteristics and relationship features, thus making the results inaccurate. By analyzing the problems in the processing of time data stream, we put forward the concept of time gap degrees and design a mining algorithms based on weighted FP-Tree. We introduce the concept of FP-Tree node weights to transform the time data dynamically and excavate the data stream association rules. The experiments performed on the actual data set show that the algorithm can improve the recall and precision while consumes comparable computational time.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Related Descriptions
• 2.1. Time Gap Degree
• 2.2. The Relevant Definitions
• 3. Weighted FP-Tree Algorithm
• 3.1. The Description of FP-Growth Algorithm
• 3.2. The Contribution Process of Weighted FP-Tree
• 3.3. Weighted FP-Growth Algorithm
• 3.4. The Prototype Case of FPSPAN-Tree
• 4. Experiments
• 4.1. The Space Complexity Analysis of the FPSPAN-Growth Algorithm
• 4.2. The Time Complexity Analysis of the FPSPAN-Growth Algorithm
• 4.3. Simulation Experiments
• 4.4. Executive Time Experiment
• 4.5. Comparison of Generation Rules
• 4.6. The Evaluation of the Derivation Rules
• 5. Conclusion
• References