DATANA DATA MINING (LESSON 1 FINALS)

DATA MINING

The process of discovering interesting patterns and knowledge from large amounts of data.

KNOWLEDGE DISCOVERY

A broader process that includes data mining as a step to extract useful information from raw data.

INFORMATION AGE

The current era characterized by the rapid shift from traditional industry to an economy based on information technology.

DATA AGE

A time where data generation is massive and constant, due to digital transformation.

DATA CLEANING, DATA INTEGRATION, DATA SELECTION, DATA TRANSFORMATION, DATA MINING, PATTER EVALUATION, KNOWLEDGE PRESENTATION

The knowledge discovery process is shown in the figure above as an iterative sequence of the following steps:

DATA CLEANING

to remove noise and inconsistent data

DATA INTEGRATION

where multiple data sources may be combined.

DATA SELECTION

where data relevant to the analysis task are retrieved from the database.

DATA TRANSFORMATION

where data are transformed and consolidated into forms appropriate for mining by performing summary or aggregation operations.

DATA MINING

an essential process where intelligent methods are applied to extract data patterns.

PATTERN EVALUATION

to identify the truly interesting patterns representing knowledge based on interestingness measures.

KNOWLEDGE PRESENTATION

where visualization and knowledge representation techniques are used to present mined knowledge to users.

DATABASE

Organized collection of data stored electronically.

RELATIONAL DATABASE

is a collection of tables, each of which is assigned a unique name.

DATA WAREHOUSE

Centralized storage integrating data from multiple sources for analysis.

TRANSACTIONAL DATA

Data capturing business transactions.

DATA CUBE

provides a multidimensional view of data and allows precomputation and fast access to summarized data.

SEQUENCE DATA, DATA STREAMS, SPATIAL DATA, ENGINEERING DESIGN DATA, MULTIMEDIA DATA , GRAPH DATA, NETWORKED DATA

Kinds of Data that can be seen in many application: