Data mining; can be defined as the acquisition of previously unknown, valid and applicable information from a heap of data through a dynamic process. In this process, different techniques such as clustering, classification, learning data summarizing rules, finding dependency networks, variability analysis and anomaly detection are used. Data mining is not a solution on its own, but a tool to support the decision process to achieve resolution and to provide the necessary information to solve the problem.
Data Mining in the Retail Sector offers an excellent application because it collects a lot of data from sales, customer purchasing date, goods transport, consumption and services. It is natural that the amount of data collected will continue to expand exponentially due to the increased ease of use, availability and popularity of the Internet. Data mining in the retail industry helps to identify customer purchasing patterns and trends that increase customer service quality, to increase customer satisfaction and to increase the quality of customer services.
In today’s world, telecommunication is one of the most innovative sectors which provides a variety of services such as fax, pager, mobile phone, internet messenger, image, e-mail, web data transmission and so on. Due to the development of new computer and communication technologies, the telecommunication sector is expanding rapidly. This is the reason why data mining is crucial to help and understand the business.
Biological Data Analysis
Recently, we have witnessed tremendous growth in the field of biologics such as genomics, proteomics, functional genomics and biomedical research. Biological data mining is a very important part of bioinformatics.
Data mining applications for the energy sector
In the oil and gas industry, large amounts of unstructured information which are integrated into traditional and structural data, present a clear and complete picture of the process. Data mining offers a solid support for the upstream oil and gas industry. It provides services such as the determination of the structure of the important information, fastening up the technical problem-solving process, strengthening decision making in a more conscious way and prompting notification of technical breakthroughs.
Data Mining System Selection
The choice of the data mining system depends on the following features -
Types of Data: The data mining system can analyze formatted text, record-based data, and relational data. The data can also be in ASCII text, relational database data, or data warehouse. For this reason, the format of the data mining system should be controlled.
System Challenges: We must consider the compatibility of the data mining system which has different operating systems. A data mining system can be run on only one operating system or in a few. There are data mining systems that provide Web-based user interfaces and provide XML data as input.
Data Sources: Data sources refers to the data formats that the data mining system will work. Some data mining systems can only work with ASCII text files, while others can work with multiple relational sources. The data mining system must also support ODBC connections or OLE DB for ODBC connections.
Data mining functions and methodologies: There are some data mining systems that provide only a single data mining function, such as classification. In some cases, multiple data mining functions such as concept definition, discovery-oriented OLAP analysis, association mining, link analysis, statistical analysis, classification are presented. Forecasting, clustering, contrary analysis, similarity research, etc.
Data mining systems with databases or data warehousing systems –
Data mining systems must be combined with a database or a data warehouse system. The merged components are integrated into a uniform computing environment.Data mining applications are constantly evolving in various industries to provide more confidential information that enhances business efficiency and grows businesses.