Discovering Changes in Association Rules Over Time from a Property Database for Price Prediction
Area: Real Estate
Association rule mining is an important topic in data mining research. Many algorithms have been developed for such task and they typically assume that the underlying associations hidden in the data are stable over time. However, in real-world domains, it is possible that the data characteristics and hence the associations change significantly over time. Existing data mining algorithms have not taken the changes in associations into consideration and this can result in severe degradation of performance, especially when the discovered association rules are used for classification (prediction). Although the mining of changes in associations is an important problem because it is common that we need to predict the future based on the historical data in the past, existing data mining algorithms are not developed for this task. In this project, we developed a new fuzzy data mining technique to discover changes in association rules over time.
- Special Features and Advantages
Discover changing patterns from time-series data.
Utilizes an objective measure to determine the interestingness of associations.
Allows the ranking of discovered rules according to an uncertainty measure.
Able to handle high-dimensional data sets efficiently.
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