What Does The Scalability Of A Data Mining Method Refer To?

Information Mining - Applications & Trends

Data mining is widely used in various areas. At that place are a number of commercial data mining system available today and all the same there are many challenges in this field. In this tutorial, we volition discuss the applications and the tendency of data mining.

Information Mining Applications

Hither is the list of areas where data mining is widely used −

Financial Data Analysis
Retail Industry
Telecommunications Industry
Biological Information Analysis
Other Scientific Applications
Intrusion Detection

Financial Data Analysis

The financial information in cyberbanking and financial manufacture is mostly reliable and of loftier quality which facilitates systematic data analysis and data mining. Some of the typical cases are as follows −

Blueprint and structure of data warehouses for multidimensional information assay and data mining.
Loan payment prediction and customer credit policy assay.
Nomenclature and clustering of customers for targeted marketing.
Detection of money laundering and other financial crimes.

Retail Manufacture

Data Mining has its groovy application in Retail Manufacture because it collects large corporeality of information from on sales, customer purchasing history, goods transportation, consumption and services. It is natural that the quantity of data collected will go on to expand rapidly because of the increasing ease, availability and popularity of the web.

Data mining in retail industry helps in identifying customer ownership patterns and trends that lead to improved quality of client service and expert customer retentiveness and satisfaction. Hither is the listing of examples of data mining in the retail industry −

Design and Construction of data warehouses based on the benefits of data mining.
Multidimensional assay of sales, customers, products, time and region.
Assay of effectiveness of sales campaigns.
Customer Retention.
Product recommendation and cross-referencing of items.

Telecommunications Industry

Today the telecommunication manufacture is one of the most emerging industries providing diverse services such as fax, pager, cellular phone, cyberspace messenger, images, e-mail, spider web data transmission, etc. Due to the evolution of new estimator and communication technologies, the telecommunication industry is rapidly expanding. This is the reason why data mining is become very of import to aid and understand the business organization.

Data mining in telecommunication industry helps in identifying the telecommunication patterns, take hold of fraudulent activities, make better use of resource, and improve quality of service. Hither is the list of examples for which information mining improves telecommunication services −

Multidimensional Analysis of Telecommunication data.
Fraudulent pattern analysis.
Identification of unusual patterns.
Multidimensional clan and sequential patterns analysis.
Mobile Telecommunication services.
Utilize of visualization tools in telecommunication data assay.

Biological Data Assay

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

Semantic integration of heterogeneous, distributed genomic and proteomic databases.
Alignment, indexing, similarity search and comparative assay multiple nucleotide sequences.
Discovery of structural patterns and assay of genetic networks and protein pathways.
Clan and path analysis.
Visualization tools in genetic information assay.

Other Scientific Applications

The applications discussed above tend to handle relatively small-scale and homogeneous data sets for which the statistical techniques are appropriate. Huge amount of data have been collected from scientific domains such equally geosciences, astronomy, etc. A big amount of information sets is being generated considering of the fast numerical simulations in various fields such as climate and ecosystem modeling, chemic engineering science, fluid dynamics, etc. Following are the applications of data mining in the field of Scientific Applications −

Data Warehouses and information preprocessing.
Graph-based mining.
Visualization and domain specific noesis.

Intrusion Detection

Intrusion refers to whatsoever kind of activeness that threatens integrity, confidentiality, or the availability of network resources. In this globe of connectivity, security has become the major issue. With increased usage of internet and availability of the tools and tricks for intruding and attacking network prompted intrusion detection to become a disquisitional component of network administration. Here is the list of areas in which data mining technology may be applied for intrusion detection −

Development of data mining algorithm for intrusion detection.
Association and correlation assay, aggregation to assist select and build discriminating attributes.
Analysis of Stream data.
Distributed data mining.
Visualization and query tools.

Data Mining System Products

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

Choosing a Data Mining Organization

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

Data Types − The data mining organisation may handle formatted text, record-based data, and relational data. The information could also be in ASCII text, relational database data or data warehouse data. Therefore, nosotros should bank check what verbal format the information mining organisation tin can handle.
System Issues − Nosotros must consider the compatibility of a information mining organisation with different operating systems. I data mining system may run on simply i operating system or on several. In that location are also information mining systems that provide web-based user interfaces and permit XML data every bit 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 arrangement 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 1 data mining office such as classification while some provides multiple data mining functions such equally concept description, discovery-driven OLAP analysis, association mining, linkage analysis, statistical assay, classification, prediction, clustering, outlier analysis, similarity search, etc.
Coupling information mining with databases or data warehouse systems − Information mining systems need to be coupled with a database or a data warehouse system. The coupled components are integrated into a uniform data processing surroundings. Here are the types of coupling listed below −
- No coupling
- Loose Coupling
- Semi tight Coupling
- Tight Coupling
Scalability − At that place are 2 scalability issues in information mining −
- Row (Database size) Scalability − A data mining system is considered as row scalable when the number or rows are enlarged 10 times. It takes no more 10 times to execute a query.
- Column (Dimension) Salability − A information mining arrangement is considered as column scalable if the mining query execution time increases linearly with the number of columns.
Visualization Tools − Visualization in data mining can exist categorized every bit follows −
- Information Visualization
- Mining Results Visualization
- Mining process visualization
- Visual data mining
Data Mining query language and graphical user interface − An easy-to-utilize graphical user interface is of import to promote user-guided, interactive data mining. Unlike relational database systems, data mining systems do non share underlying data mining query language.

Trends in Data Mining

Information mining concepts are yet evolving and hither are the latest trends that we get to see in this field −

Awarding Exploration.
Scalable and interactive data mining methods.
Integration of information 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 information.
Biological data mining.
Data mining and software technology.
Web mining.
Distributed data mining.
Real time information mining.
Multi database data mining.
Privacy protection and information security in information mining.