Introduction to data mining and its applications / Введение в интеллектуальный анализ данных и его приложения

Objectives:

• This section deals with detailed study of the principles of data warehousing, data mining, and knowledge discovery.

• The availability of very large volumes of such data has created a problem of how to extract useful, task-oriented knowledge.

• The aim of data mining is to extract implicit, previously unknown and potentially useful patterns from data.

• Data warehousing represents an ideal vision of maintaining a central repository of all organizational data.

• Centralization of data is needed to maximize user access and analysis.

• Data warehouse is an enabled relational database system designed to support very large databases (VLDB) at a significantly higher level of performance and manageability.

• Due to the huge size of data and the amount of computation involved in knowledge discovery, parallel processing is an essential component for any successful large-scale data mining application.

• Data warehousing provides the enterprise with a memory. Data mining provides the enterprise with intelligence.

• Data mining is an interdisciplinary field bringing together techniques from machine learning, pattern recognition, statistics, databases, visualization, and neural networks.

• We analyze the knowledge discovery process, discuss the different stages of this process in depth, and illustrate potential problem areas with examples.<...>