Rapid developments in technology collection and storage of data has been easier for organizations to collect large sejumlahdata so as to produce useful information data.Ekstraksi mountain of mountains of data into information menantang.mengekstrak enough job of measuring data besar.DATA MINING is a technology that is a mixture of methods of data analysis algorithms-algorithms for processing large data.
DATA DEFINITIONS MNING
DATA MINING Is A Process Automatically search Useful information in the data storage place Besar.Istilah Sized other frequently used among Knowladge discovery (mining) in databases (KDD) .Teknik DATA MINING used to examine large data base as a way to find a pattern new and berguna.tidak all work is expressed as a data search information mining.Sebagai examples of individual records search using a database management system or web page search query tertentumelalui everyone is a job search engine that eratkaitannya information with information retrieval.Teknik-engineering data mining can be used to improve the ability of the system - the system of information retrieval.
DATA MINING SYSTEM ARCHITECTURE
DATA MINING is a process of finding interesting knowledge from the data size stored in the database, data warehouse or a data mining lainnya.arsitektur penyimpananinformasi have components - the main components, namely:
1.Basis Data, ~ data warehouse or other information storage.
2.Basis data and data warehouse server, ~ This component is responsible for the collection of relevant data, based on user demand.
Knowledge 3.Basis, ~ domain knowladge This component is used to guide the search or mengevaluasian pattern generated pattern.
4.Data mining Engine, ~ This section is an important component in the system architecture of data mining.
Evaluation 5.Modul pattern, ~ This component uses the size of the size of the attractiveness and interact with the data module search miningdalam interesting pattern patterns.
6.Antar Graphical User Front, ~ This module berkomuniaksi with user and system data mining.
PROJECT - TASK DATA MINING
Data mining tasks are generally divided into two main categories:
1.Prediktif which aims to predict the value of a specific attribute based on the value of other attributes.
2.Deskriptif which aims to reduce the pattern pattern (correlation, trend, clutser, trajectory and anomalies) that summarizes the data pokokdalam relationship.
NEXT TASK TASK IN DATA MINING:
1.Analisis Association
Search Rules association rules that indicates the condition of the condition nilia attributes that often occur together in a set of data.
2.Klasifikasi and predictions
The process of finding models that describe and distinguish classes or concepts class, with the aim that the model obtained can be used to predict the class or object that has a class label is unknown.
3.Analisa Clutser
Analyzing data object where the class label is unknown.
4.Analisia Outlier
A data object that does not follow the general behavior of data.Outlier can be considered as noise or exception.
5.Analisis Trend and Evolution
Menjelaskna and trend modeling of objects that have changed behavior every time.
