Publications by authors named "Jimmy Ming-Tai Wu"

Top- dominating (TKD) query is one of the methods to find the interesting objects by returning the objects that dominate other objects in a given dataset. Incomplete datasets have missing values in uncertain dimensions, so it is difficult to obtain useful information with traditional data mining methods on complete data. BitMap Index Guided Algorithm (BIG) is a good choice for solving this problem.

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The accuracy of the prediction of stock price fluctuations is crucial for investors, and it helps investors manage funds better when formulating trading strategies. Using forecasting tools to get a predicted value that is closer to the actual value from a given financial data set has always been a major goal of financial researchers and a problem. In recent years, people have paid particular attention to stocks, and gradually used various tools to predict stock prices.

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Article Synopsis
  • Privacy-preserving data mining is crucial for safeguarding sensitive information while still allowing for meaningful data analysis, but it poses significant challenges as an NP-hard problem.
  • Many existing evolutionary algorithms address this issue but tend to focus on single-objective functions with set weight values, limiting their effectiveness.
  • The paper introduces a new multiple objective particle swarm optimization method (CMPSO) that utilizes a density clustering approach, demonstrating better performance in hiding sensitive information by adapting to user preferences through extensive testing on two datasets.
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High utility itemset mining has become an important and critical operation in the Data Mining field. High utility itemset mining generates more profitable itemsets and the association among these itemsets, to make business decisions and strategies. Although, high utility is important, it is not the sole measure to decide efficient business strategies such as discount offers.

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