Data Mining

THE DATA MINING PERFORMS AN IMPORTANT ROLE IN THE WORLD

Data Mining is the process of extracting knowledge hidden from large volumes of raw data.The knowledge must be new, not obvious, and one must be able to use it. 

Knowledge discovery differs from traditional information retrieval from databases.In traditional DBMS, database records are returned in response to a query; while in knowledge discovery, what is retrieved is not explicit in the database. Rather, it is implicit patterns. The Process of discovering such patterns is termed data mining. 

Data mining finds these patterns and relationships using data analysis tools and techniques to build models. There are two main kinds of models in data mining. One is predictive models, which use data with known results to develop a model that can be used to explicitly predict values. Aother is descriptive models, which describe patterns in existing data. All the models are abstract representations of reality, and can be guides to understanding business and suggest actions.

Following questions are probably be answered if information hidden among megabytes of data in your database can be found and utilized. Modeling the investigated system, discovering relations that connect variables in a database are the subject of data mining.

• What goods should be promoted to this customer?
• What is the probability that a certain customer will respond to a planned promotion?
• Can one predict the most profitable securities to buy/sell during the next trading session?
• Will this customer default on a loan or pay back on schedule?
• What medical diagnose should be assigned to this patient?
• How large the peak loads of a telephone or energy network are going to be?
• Why the facility suddenly starts to produce defective goods?

DATABASE CONCEPTS

What's a database ?

A database is a collection of data organized in a particular way. Databases can be of many types such as Flat File Databases, Relational Databases, Distributed Databases etc.

What's SQL ?
SQL is the short form of srtuctured query language.

In 1971, IBM researchers created a simple non-procedural language called Structured English Query Language. or SEQUEL. This was based on Dr. Edgar F. (Ted) Codd's design of a relational model for data storage where he described a universal programming language for accessing databases.

In the late 80's ANSI and ISO (these are two organizations dealing with standards for a wide variety of things) came out with a standardized version called Structured Query Language or SQL. SQL is prounced as 'Sequel'. There have been several versions of SQL and the latest one is SQL-99. Though SQL-92 is the current universally adopted standard.

SQL is the language used to query all databases. It's simple to learn and appears to do very little but is the heart of a successful database application. Understanding SQL and using it efficiently is highly imperative in designing an efficient database application. The better your understanding of SQL the more versatile you'll be in getting information out of databases.

What's an RDBMS ?

This concept was first described around 1970 by Dr. Edgar F. Codd in an IBM research publication called "System R4 Relational".

A relational database uses the concept of linked two-dimensional tables which comprise of rows and columns. A user can draw relationships between multiple tables and present the output as a table again. A user of a relational database need not understand the representation of data in order to retrieve it. Relational programming is non-procedural.

Whatis a DBMS ?

MySQL and mSQL are database management systems or DBMS. These software packages are used to manipulate a database. All DBMSs use their own implementation of SQL. It may be a subset or a superset of the instructions provided by SQL 92. MySQL, due to it's simplicity uses a subset of SQL 92 (also known as SQL2).

What's Database Normalization ?

Normalization is the process where a database is designed in a way that removes redundancies, and increases the clarity in organizing data in a database.

In easy English, it means take similar stuff out of a collection of data and place them into tables. Keep doing this for each new table recursively and you'll have a Normalized database. From this resultant database you should be able to recreate the data into it's original state if there is a need to do so.

The important thing here is to know when to Normalize and when to be practical. That will come with experience. For now, read on...

Normalization of a database helps in modifying the design at later times and helps in being prepared if a change is required in the database design. Normalization raises the efficiency of the datatabase in terms of management, data storage and scalability.