Instance-Based Learning Algorithms
Machine Learning
Dimensionality reduction of symbolic data
Pattern Recognition Letters
A conceptual version of the K-means algorithm
Pattern Recognition Letters
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Conversion methods for symbolic features: A comparison applied to an intrusion detection problem
Expert Systems with Applications: An International Journal
Automated Construction of Classifications: Conceptual Clustering Versus Numerical Taxonomy
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The accuracy of a classifier relies heavily on the encoding and representation of input data. Many machine learning algorithms require that the input vectors be composed of numeric values on which arithmetic and comparison operators be applied. However, many real life applications involve the collection of data, which is symbolic or 'nominal type' data, on which these operators are not available. This paper presents a framework called logical expression feature transformation (LEFT), which can be used for mapping symbolic attributes to a continuous domain, for further processing by a learning machine. It is a generic method that can be used with any suitable clustering method and any appropriate distance metric. The proposed method was tested on synthetic and real life datasets. The results show that this framework not only achieves dimensionality reduction but also improves the accuracy of a classifier.