Data Mining Algorithms 1: Preliminaries

These flashcards cover key concepts, definitions, and techniques discussed in the lecture on Data Mining Algorithms, preparing the student for their upcoming exam.

What is data?

A representation of real or artificial objects, situations, and processes, measured, recorded, or generated through various means.

What are the types of data representation?

Data can be represented in numerical and categorical types, similarity models, and undergo data reduction for efficiency.

What is data visualization?

The process of transforming data into visually perceivable representations to identify patterns more easily.

What is a metric space?

A set of objects equipped with a distance function that satisfies properties like symmetry, identity of indiscernibles, and triangle inequality.

What is the difference between numerical and categorical data?

Numerical data consists of numbers (e.g., age, income) while categorical data consists of symbols and identifiers (e.g., subjects, occupations).

What is meant by 'Euclidean distance'?

A measure of the straight-line distance between two points in Euclidean space.

What is dimension reduction?

The process of reducing the number of attributes in a dataset, making data analysis more manageable and efficient.

What does OLAP stand for?

Online Analytical Processing, which refers to tools that allow users to analyze data from multiple perspectives.

What are some examples of visualization techniques?

Scatter plots, parallel coordinates, pixel-oriented visualizations, and Chernoff faces.

How does data aggregation assist in data reduction?

It simplifies the data by summarizing information, thus enhancing the ability to analyze and visualize patterns.

What is the purpose of using a similarity query?

To find objects in a database that are similar to a given query object based on a defined distance function.

What are basic aggregates used in data analysis?

Measures like mean, median, and mode that summarize data, providing insights about its central tendency and distribution.

What is the significance of normalization in data handling?

Normalization adjusts the scale of data attributes, making them comparable and improving the performance of algorithms.

What defines a generalization hierarchy in data?

A system where attributes are organized in levels, allowing for abstraction and data summarization.

What is the role of feature extraction in data mining?

It transforms complex data into a simpler feature vector representation to facilitate analysis and comparisons.