Abstract
Feature selection is an important method to provide both efficiency and effectiveness for high-dimension data clustering. However, most feature selection methods require prior knowledge such as class-label information to train the clustering module, where its performance depends on training data and types of learning machine. This paper presents a feature selection algorithm that does not require supervised feature assessment. We analyze relevance and redundancy among features and effectiveness to each target class to build a correlation-based filter. Compared to feature sets selected by existing methods, the experimental results show that performance of a feature set selected by the proposed method is comparably equal and better when it is tested on the RCV1v2 corpus and Isolet data set, respectively. However, our technique is simpler and faster and it is independent to types of learning machine.
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Pramokchon, P., Piamsa-nga, P. (2014). An Unsupervised, Fast Correlation-Based Filter for Feature Selection for Data Clustering. In: Herawan, T., Deris, M., Abawajy, J. (eds) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). Lecture Notes in Electrical Engineering, vol 285. Springer, Singapore. https://doi.org/10.1007/978-981-4585-18-7_10
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DOI: https://doi.org/10.1007/978-981-4585-18-7_10
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