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Generalized Variable Conversion Using K-means Clustering and Web Scraping

  • Kourosh ModarresiEmail author
  • Abdurrahman Munir
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)

Abstract

The world of AI and Machine Learning is the world of data and learning from data so the insights could be used for analysis and prediction. Almost all data sets are of mixed variable types as they may be quantitative (numerical) or qualitative (categorical). The problem arises from the fact that a long list of methods in Machine Learning such as “multiple regression”, “logistic regression”, “k-means clustering”, and “support vector machine”, all to be as examples of such models, designed to deal with numerical data type only. Though the data, that need to be analyzed and learned from, is almost always, a mixed data type and thus, standardization step must be undertaken for all these data sets. The standardization process involves the conversion of qualitative (categorical) data into numerical data type.

Keywords

Mixed variable types NLP K-means clustering 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Adobe Inc.San JoseUSA

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