Data mining :

1. Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.
2. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
3. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.
4. he actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining).
5. This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics.
6. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.
7. The Knowledge Discovery in Databases (KDD) process is commonly defined with the stages:

(1) Selection (2) Pre-processing (3) Transformation (4) Data Mining (5) Interpretation/Evaluation.

Data mining step of KDD:

Data mining involves six common classes of tasks:

1. Anomaly detection (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.

2. Association rule learning (Dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can 3. determine which products are frequently bought together and use this information for marketing purposes. This is

3. Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.

4. Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".

5. Regression – attempts to find a function which models the data with the least error.

6. Summarization – providing a more compact representation of the data set, including visualization and report generation.