Handling categorical attributes

Difference between continuous and categorical attributes

Continuous attributes are attributes that can take on any value within a range. For example, the height of a person is a continuous attribute. Categorical attributes are attributes that can only take on a limited number of values. For example, the eye color of a person is a categorical attribute.

Concept hierarchy

A concept hierarchy is a tree-like structure that represents the relationship between different concepts. The root node of the tree represents the most general concept, and the leaf nodes represent the most specific concepts. The intermediate nodes represent more specific concepts that are related to the more general concepts.

Major patterns of data and how they work

There are a number of major patterns of data that can be identified. Some of the most common patterns include:

• Linear relationships: Linear relationships are relationships between two variables that can be represented by a straight line. For example, the relationship between the height and weight of a person is a linear relationship.
• Non-linear relationships: Non-linear relationships are relationships between two variables that cannot be represented by a straight line. For example, the relationship between the price of a product and the number of units sold is a non-linear relationship.
• Association rules: Association rules are rules that describe the relationships between multiple variables. For example, an association rule might state that if a person buys milk, they are likely to also buy bread.
• Clustering: Clustering is the process of grouping similar data points together. For example, a clustering algorithm might group customers together based on their buying habits.
• Anomaly detection: Anomaly detection is the process of identifying data points that are unusual or unexpected. For example, an anomaly detection algorithm might identify credit card transactions that are likely to be fraudulent.

These are just a few of the major patterns of data that can be identified. The specific patterns that are present in a particular dataset will depend on the data itself.

Techniques in handling categorical attributes

There are a number of techniques that can be used to handle categorical attributes. Some of the most common techniques include:

• Label encoding: This is the simplest technique. It involves assigning a unique integer value to each category. For example, if the categorical attribute is "color" and the possible categories are "red", "green", and "blue", then the label encoding would be 1, 2, and 3, respectively.
• One-hot encoding: This technique creates a new binary attribute for each category. The new attribute is 1 if the category is present and 0 if it is not present. For example, if the categorical attribute is "color" and the possible categories are "red", "green", and "blue", then the one-hot encoding would create three new attributes: "is_red", "is_green", and "is_blue".
• Hashing: This technique converts the categorical attribute into a hash value. The hash value is then used as the new attribute. Hashing can be used to handle categorical attributes with a large number of categories.
• Decision trees: Decision trees can be used to learn the relationship between categorical attributes and a target variable. Decision trees can be used to predict the value of the target variable for new data.