ORACLE DATA MINING (ODM)

As a general look, data is raw information, however information is the result of processing, manipulating and organizing data which forms knowledge. Real-world applications consist of complex data.

Data mining means extracting information from large data sets and databases. Market Basket Analysis is an example of data mining used in sales : If men buy beer, diaper sales also increases.
Data mining is embedded in Oracle database and is one of the area of data warehouse. Data is stored in tables although there are many different types of information and does not leave database while the operation taking place. Also, the number of columns you mine is not important in Oracle Data Mining (ODM).

There should be a problem waiting to be solved. In addition to this, there are some basic steps for the solution. Data gathering is the first step. The next step will be to choose one of the data mining algorithms. Always remember, there is no one perfect algorithm for problems. The choice of algorithm can change according to the type of data and problem. Data should be filtered and normalized before used in algorithms.

Humans and machines are different from each other. Although one of them has ability to sense, identify and think , the other one behaves through what he learned. There are kinds of functions and algorithms required by Oracle Data Mining. Data mining applies machine learning concepts to data. There are two types of Data Mining functions. These learnings are generated from machine learning area. One of them is supervised data mining which is direct learning, making prediction. The second one is unsupervised learning which is used for descriptive uses and non-directed.
The conspicuous algorithms used in data mining are Naive Bayesian, Decision Tree, Clustering (k-means) ect.