CAP3770 - Data Mining Lecture - Chapter 1

Vocabulary flashcards for reviewing key data mining concepts and definitions

Data Generation Advances

Enormous data growth in both commercial and scientific databases due to advances in data generation and collection technologies.

Data Gathering

Gather whatever data you can whenever and wherever possible.

Commercial Data Mining Viewpoint

Data mining helps businesses provide better, customized services for an edge in Customer Relationship Management.

Scientific Data Mining Viewpoint

Data mining assists scientists in automated analysis of massive datasets and in hypothesis formation.

Data

The 'Facts'.

Information

Interpretation of Data.

Knowledge

Information That Has Been Given Meaning.

Explicit Knowledge

Identified and Codified Information, Documents, Records, and Files.

Tacit Knowledge

Lives in people and their practices; Experiences, Competence, Commitment, Deeds, and Thoughts.

Data Mining

Non-trivial extraction of implicit, previously unknown and potentially useful information from data; Exploration & analysis, by automatic or semi-automatic means, of large quantities of data to discover meaningful patterns.

Data Cleaning

Process that removes or transforms noise and inconsistent data.

Data Integration

Process where multiple data sources may be combined.

Data Selection

Data relevant to the analysis task are retrieved from the database.

Data Transformation

Data transformed/consolidated into appropriate forms for mining.

Data Mining (Process)

Essential process where intelligent and efficient methods are applied in order to extract patterns.

Pattern Evaluation

Process that identifies the truly interesting patterns representing knowledge based on some interestingness measures.

Knowledge Presentation

Visualization and knowledge representation techniques are used to present the mined knowledge to the user.

Prediction Methods

Use some variables to predict unknown or future values of other variables.

Description Methods

Find human-interpretable patterns that describe the data.

Classification

Find a model for class attribute as a function of the values of other attributes; Predictive Modeling

Data Mining Steps (abridged)

Understanding purpose, obtaining data, cleaning & preprocessing, reducing dimension, determining task, partitioning data, choosing technique, using algorithms, interpreting results, deploying model

Regression

Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency.

Clustering

Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups.

Association Rule Discovery

Produce dependency rules which will predict occurrence of an item based on occurrences of other items, given a set of records.

Deviation/Anomaly/Change Detection

Detect significant deviations from normal behavior

Scalability

Ability to increase or decrease in response to change.

High Dimensionality

The number of dimensions are so high that calculations become difficult.

Heterogeneous and Complex Data

Any data with high variability of data types and formats.