# KNOWLEDGE DISCOVERY DATA

Mind Map by Rosalía Iñiguez, updated more than 1 year ago
 Created by Rosalía Iñiguez over 6 years ago
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### Description

Principales aspectos de la obtención del conocimiento con Knowledge Discovery Data

## Resource summary

KNOWLEDGE DISCOVERY DATA
1. ITERATIVE SEQUENCE OF STEPS
1. DATA CLEANING
1. DATA INTEGRATION
1. DATA SELECTION
1. DATA TRANSFORMATION
1. DATA MINING

Annotations:

• PROCESS OF DISCOVERING INTERESTING PATTERN AND KNOWLEDGE FROM LARGE AMOUNTS OF DATA
1. DESCRIPTIVES
1. PREDICTIVES
1. DOMAINS
1. STATISTICS

Annotations:

•  Statistics studies the collection, analysis, interpretation or explanation, and presentation of data
1. MACHINE LEARNING

Annotations:

•  Machinelearning investigates how computers can learn (or improve their performance) based on data
1. PATTERN RECOGNITION
1. DATABASE
1. DATA WAREHOUSE
1. INFORMATION RETRIEVAL
1. VISUALIZATION
1. ALGORITHMS
1. HIGH PERFORMANCE COMPUTING
2. PATTERNS CAN BE MINED DATA MINING FUNCTIONALITIES
1. DISCRIMINATION

Annotations:

• DISCRIMINATION: COMPARISON OF FEATURES OF ONE CLASS DATA OBJETC AGAINST GENERAL FEATURES OF OBJECTS FROM ONE OR MULTIPLE CLASS OBJECTS CHARACTERIZATION:  summarizing the data of the class under study (often called the target class) in general terms
1. FREQUEN PATTERNS

Annotations:

•  There are many kinds of frequent patterns, including frequent itemsets, frequent subsequences (also known as sequential patterns), and frequent substructures.
1. SUPPORT
1. CONFIDENCE
1. accuracy and coverage
2. ASSOCIATIONS
1. CORRELATIONS
1. CLASSIFICATION AND REGRESSION

Annotations:

•  Classification is the process of finding a model (or function) that describes and distinguishes data classes or concepts.  Regression analysis is astatistical methodology that is most often used for numeric prediction,
1. CLUSTERING ANALYSIS AND OULIER ANALYSIS

Annotations:

•  Unlike classification and regression, which analyze class-labeled (training) data sets, clustering analyzes data objects without consulting class labels.
1. INTERESTING PATTERNS
1. NOVEL
1. CERTAINTY
1. POTENTIALLY USEFUL
1. EASILY UNSDERSTOOD
1. PATTERN INTERSTINGNESS
1. SUBJECTIVE
1. OBJECTIVE
2. DATA CAN BE MINED
1. DATABASES
1. DATA WAREHOUSES
1. TRANSACTIONAL DATA
1. MANY OTHERS
2. ISSUES OF DATA MINING RESEARCH
1. MINING METHODOLOGIES
1. USER INTERACTION
1. EFFICIENCY AND SCALABILITY
1. DIVERSITY OF DATA TYPES
1. DATA MINING AND SOCIETY
2. VIEWS
1. APPLICATION
1. TECHNOLOGIES
1. DATA
1. KNOWLEDGE
2. PATTERN EVALUATION

Annotations:

• ¿Interesante?:  (1) easily understood byhumans, (2) valid on new or test data with some degree of certainty, (3) potentiallyuseful, and(4) novel. A pattern is also interesting if it validates a hypothesis that the user sought to confirm.
1. KNOWLEDGE PRESENTATION
2. APPLICATIONS
1. WEB SEARCH
1. BIOINFORMATICS
1. HEALTH INFORMATICS
1. FINANCE
1. DIGITAL LIBRARIES
1. DIGITAL GOVERMENT

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