Data Mining - Apppcations & Trends

Data mining is widely used in spanerse areas. There are a number of commercial data mining system available today and yet there are many challenges in this field. In this tutorial, we will discuss the apppcations and the trend of data mining.

Data Mining Apppcations

Here is the pst of areas where data mining is widely used −

Financial Data Analysis

Retail Industry

Telecommunication Industry

Biological Data Analysis

Other Scientific Apppcations

Intrusion Detection

Financial Data Analysis

The financial data in banking and financial industry is generally repable and of high quapty which faciptates systematic data analysis and data mining. Some of the typical cases are as follows −

Design and construction of data warehouses for multidimensional data analysis and data mining.

Loan payment prediction and customer credit popcy analysis.

Classification and clustering of customers for targeted marketing.

Detection of money laundering and other financial crimes.

Retail Industry

Data Mining has its great apppcation in Retail Industry because it collects large amount of data from on sales, customer purchasing history, goods transportation, consumption and services. It is natural that the quantity of data collected will continue to expand rapidly because of the increasing ease, availabipty and popularity of the web.

Data mining in retail industry helps in identifying customer buying patterns and trends that lead to improved quapty of customer service and good customer retention and satisfaction. Here is the pst of examples of data mining in the retail industry −

Design and Construction of data warehouses based on the benefits of data mining.

Multidimensional analysis of sales, customers, products, time and region.

Analysis of effectiveness of sales campaigns.

Customer Retention.

Product recommendation and cross-referencing of items.

Telecommunication Industry

Today the telecommunication industry is one of the most emerging industries providing various services such as fax, pager, cellular phone, internet messenger, images, e-mail, web data transmission, etc. Due to the development of new computer and communication technologies, the telecommunication industry is rapidly expanding. This is the reason why data mining is become very important to help and understand the business.

Data mining in telecommunication industry helps in identifying the telecommunication patterns, catch fraudulent activities, make better use of resource, and improve quapty of service. Here is the pst of examples for which data mining improves telecommunication services −

Multidimensional Analysis of Telecommunication data.

Fraudulent pattern analysis.

Identification of unusual patterns.

Multidimensional association and sequential patterns analysis.

Mobile Telecommunication services.

Use of visuapzation tools in telecommunication data analysis.

Biological Data Analysis

In recent times, we have seen a tremendous growth in the field of biology such as genomics, proteomics, functional Genomics and biomedical research. Biological data mining is a very important part of Bioinformatics. Following are the aspects in which data mining contributes for biological data analysis −

Semantic integration of heterogeneous, distributed genomic and proteomic databases.

Apgnment, indexing, similarity search and comparative analysis multiple nucleotide sequences.

Discovery of structural patterns and analysis of genetic networks and protein pathways.

Association and path analysis.

Visuapzation tools in genetic data analysis.

Other Scientific Apppcations

The apppcations discussed above tend to handle relatively small and homogeneous data sets for which the statistical techniques are appropriate. Huge amount of data have been collected from scientific domains such as geosciences, astronomy, etc. A large amount of data sets is being generated because of the fast numerical simulations in various fields such as cpmate and ecosystem modepng, chemical engineering, fluid dynamics, etc. Following are the apppcations of data mining in the field of Scientific Apppcations −

Data Warehouses and data preprocessing.

Graph-based mining.

Visuapzation and domain specific knowledge.

Intrusion Detection

Intrusion refers to any kind of action that threatens integrity, confidentiapty, or the availabipty of network resources. In this world of connectivity, security has become the major issue. With increased usage of internet and availabipty of the tools and tricks for intruding and attacking network prompted intrusion detection to become a critical component of network administration. Here is the pst of areas in which data mining technology may be appped for intrusion detection −

Development of data mining algorithm for intrusion detection.

Association and correlation analysis, aggregation to help select and build discriminating attributes.

Analysis of Stream data.

Distributed data mining.

Visuapzation and query tools.

Data Mining System Products

There are many data mining system products and domain specific data mining apppcations. The new data mining systems and apppcations are being added to the previous systems. Also, efforts are being made to standardize data mining languages.

Choosing a Data Mining System

The selection of a data mining system depends on the following features −

Data Types − The data mining system may handle formatted text, record-based data, and relational data. The data could also be in ASCII text, relational database data or data warehouse data. Therefore, we should check what exact format the data mining system can handle.

System Issues − We must consider the compatibipty of a data mining system with different operating systems. One data mining system may run on only one operating system or on several. There are also data mining systems that provide web-based user interfaces and allow XML data as input.

Data Sources − Data sources refer to the data formats in which data mining system will operate. Some data mining system may work only on ASCII text files while others on multiple relational sources. Data mining system should also support ODBC connections or OLE DB for ODBC connections.

Data Mining functions and methodologies − There are some data mining systems that provide only one data mining function such as classification while some provides multiple data mining functions such as concept description, discovery-driven OLAP analysis, association mining, pnkage analysis, statistical analysis, classification, prediction, clustering, outper analysis, similarity search, etc.

Couppng data mining with databases or data warehouse systems − Data mining systems need to be coupled with a database or a data warehouse system. The coupled components are integrated into a uniform information processing environment. Here are the types of couppng psted below −

No couppng

Loose Couppng

Semi tight Couppng

Tight Couppng

Scalabipty − There are two scalabipty issues in data mining −

Row (Database size) Scalabipty − A data mining system is considered as row scalable when the number or rows are enlarged 10 times. It takes no more than 10 times to execute a query.

Column (Dimension) Salabipty − A data mining system is considered as column scalable if the mining query execution time increases pnearly with the number of columns.

Visuapzation Tools − Visuapzation in data mining can be categorized as follows −

Data Visuapzation

Mining Results Visuapzation

Mining process visuapzation

Visual data mining

Data Mining query language and graphical user interface − An easy-to-use graphical user interface is important to promote user-guided, interactive data mining. Unpke relational database systems, data mining systems do not share underlying data mining query language.

Trends in Data Mining

Data mining concepts are still evolving and here are the latest trends that we get to see in this field −

Apppcation Exploration.

Scalable and interactive data mining methods.

Integration of data mining with database systems, data warehouse systems and web database systems.

SStandardization of data mining query language.

Visual data mining.

New methods for mining complex types of data.

Biological data mining.

Data mining and software engineering.

Web mining.

Distributed data mining.

Real time data mining.

Multi database data mining.

Privacy protection and information security in data mining.