categorization

Examples of categorization in the following topics:

• Understanding Concepts

  • Framing and categorization help us interpret, understand, and utilize concepts through accessible context and organization.
  • Simple categorization is the process of sorting or arranging things into categories.
  • Categorization is fundamental in language, prediction, inference, decision making, and other environmental interactions.
  • Thus, conceptual clustering creates a classification structure, which can then be used for categorization.
  • The pitfall of categorization, as many have experienced, is that many things in life defy categorization.

• Classification and Categorization

  • The word "categorization" implies that objects are sorted into categories, usually for some specific purpose.
  • There are many theories of how the mind categorizes objects and ideas.
  • This type of categorization dates back to the classical period in Greece.
  • Most modern forms of categorization do not have such a cut-and-dried system.
  • Categorization can also be viewed as the process of grouping things based on prototypes.

• Types of Variables

  • Therefore, categorical variables are qualitative variables and tend to be represented by a non-numeric value.
  • Categorical variables may be further described as ordinal or nominal.
  • An ordinal variable is a categorical variable.
  • A nominal variable is a categorical variable.
  • Examples of nominal categorical variables include sex, business type, eye colour, religion and brand.

• Continuous REGE for geodesic distances (Padgett's marriage data)

  • Also note that these results are finding rather different features of the data than did the categorical treatment.
  • The continuous REGE algorithm applied to the undirected data is probably a better choice than the categorical approach.
  • The result still shows very high regular equivalence among the actors, and the solution is only modestly similar to that of the categorical approach.

• Types of variables

  • This variable seems to be a hybrid: it is a categorical variable but the levels have a natural ordering.
  • To simplify analyses, any ordinal variables in this book will be treated as categorical variables.
  • Classify each of the variables as continuous numerical, discrete numerical, or categorical.
  • Are these numerical or categorical variables?
  • Thus, each is categorical variables.

• Categorical predictors with two levels

  • Categorical variables are also useful in predicting outcomes.
  • Here we consider a categorical predictor with two levels (recall that a level is the same as a category).
  • For categorical predictors with just two levels, the linearity assumption will always be satisfied.

• Contingency tables and bar plots

  • Like numerical data, categorical data can also be organized and analyzed.
  • In this section, we will introduce tables and other basic tools for categorical data that are used throughout this book.
  • A table that summarizes data for two categorical variables in this way is called a contingency table.
  • A bar plot is a common way to display a single categorical variable.
  • On the other hand, area codes and zip codes are not numerical, but rather they are categorical variables.

• Categorical REGE for directed binary data (Wasserman-Faust directed data)

• Categorical REGE for geodesic distances (Padgett's marriage data)

  • The categorical REGE algorithm (Network>Roles & Positions>Maximal Regular>CATREGE) can be used to identify regularly equivalent actors by treating the elements of the geodesic distance matrix as describing "types" of ties -- that is different geodesic distances are treated as "qualitatively" rather than "quantitatively" different.
  • Categorical multi-value analysis (geodesic distance) of Padgett marriage alliances

• Bacteroidetes and Chlorobi

  • Bacteria categorized under the Phylum Bacteroidetes and Phlyum Chlorobi are closely related base on comparative genomic analysis.
  • The bacteria categorized as bacteroidetes are opportunistic and are rarely pathogenic as they constitute part of the normal flora.
  • An image of a green sulfur bacteria which is categorized under the Phlyum Chlorobi and shares a close relationship with bacteria in the Phlyum Bacteroidetes.