Outlier Detection in Categorical, Text, and Mixed Attribute Data

  • Charu C. Aggarwal


The discussion in the previous chapters has primarily focused on numerical data. However, the setting of numerical data represents a gross oversimplification because categorical attributes are ubiquitous in real-world data. For example, although demographic data may contain quantitative attributes such as the age, most other attributes such as gender, race, and ZIP code are categorical. Data collected from surveys may often contain responses to multiple-choice questions that are categorical. Similarly, many types of data such as the names of people and entities, IP-addresses, and URLs are inherently categorical. In many cases, categorical and numeric attributes are found in the same data set. Such mixed-attribute data are often challenging to machine-learning applications because of the difficulties in treating the various types of attributes in a homogeneous and consistent way.


Frequent Pattern Outlier Detection Text Data Latent Dirichlet Allocation Latent Semantic Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Charu C. Aggarwal
    • 1
  1. 1.IBM T.J. Watson Research CenterNew YorkUSA

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