Data Mining and Machine Learning are two fields which have influenced each other. Data mining is the field in which operations are performed on sets of data to determine certain patterns in the data sets, whereas machine learning uses certain algorithms that automatically improves the analysis processes through data based experiences. Although data mining and machine learning have many common things, they are quite different from each other. Read this article to learn more about Data Mining and Machine Learning and how they are different from each other. What is Data Mining? Data Mining is the process of discovering ... Read More

The trends in data mining are as follows −Application exploration − Early data mining applications targeted generally on helping businesses gain a competitive edge. The exploration of data mining for businesses continues to expand as e-commerce and e-marketing have become mainstream components of the retail market.Data mining is increasingly used for the exploration of applications in several areas, including financial analysis, telecommunications, biomedicine, and science. Emerging software areas contain data mining for counterterrorism (including and beyond intrusion detection) and mobile (wireless) data mining. As generic data mining systems can have limitations in dealing with application-specific issues, it can view a ... Read More

There are various features of data mining that are as follows −Data types − Most data mining systems that are accessible in the industry handle formatted, record-based, relational-like data with statistical, categorical, and symbolic attributes. The data can be in the form of ASCII text, relational database data, or data warehouse data. It is essential to test what exact format(s) each system it is treating can handle.Some types of data or applications can require specialized algorithms to search for patterns, and so their requirements cannot be managed by off-the-shelf, generic data mining systems. Rather than, specialized data mining systems can ... Read More

The following are areas in which data mining technology can be used or created for intrusion detection which are as follows −Development of data mining algorithms for intrusion detection − Data mining algorithms can be used for misuse detection and anomaly detection. In misuse detection, training information is labeled as either “normal” or “intrusion.” A classifier can then be changed to detect known intrusions.There is multiple research in this area that has contained the application of classification algorithms, association rule mining, and cost-sensitive modeling. Anomaly detection constructs models of normal behavior and automatically detects significant deviations from it and supervised ... Read More

The security of our computer systems and information is at constant risk. The extensive growth of the web and increasing accessibility of tools and tricks for intruding and attacking networks have prompted intrusion detection to become an important element of network administration. An intrusion can be represented as any set of events that threaten the integrity, confidentiality, or availability of a network resource (including user accounts, file systems, system kernels, etc).Some commercial intrusion detection systems are limiting and do not support a whole solution. Such systems generally employ misuse detection approaches. Misuse detection searches for designs of program or user ... Read More

There are the following aspects of data mining for biological data analysis which are as follows −Semantic integration of heterogeneous, distributed genomic and proteomic databases − Genomic and proteomic data sets are generated at multiple labs and by various methods. They are distributed, heterogeneous, and of a wide variety. The semantic integration of such data is important to the cross-site analysis of biological records.Furthermore, it is essential to find correct linkages among research literature and their related biological entities. Such integration and linkage analysis can support the systematic and coordinated analysis of genome and biological records. This has promoted the ... Read More

The telecommunication industry has quickly evolved from providing local and long-distance telephone services to providing several other comprehensive communication services, such as fax, pager, cellular phone, web messenger, images, e-mail, computer and Web data transmission, and several data traffic.The integration of telecommunication, computer network, the Internet, and several other means of communication and computing is also underway. Furthermore, with the deregulation of the telecommunication market in several countries and the development of new computer and communication technologies, the telecommunication industry is rapidly expanding and hugely competitive.This makes a huge demand for data mining in order to support understanding the business ... Read More

The retail industry is a major application area for data mining because it collects huge amounts of records on sales, users shopping history, goods transportation, consumption, and service. The quantity of data collected continues to expand promptly, especially because of the increasing ease, accessibility, and popularity of business conducted on the internet, or e-commerce.Today, multiple stores also have websites where users can create purchases online. Some businesses, including Amazon.com (www.amazon.com), exist solely online, without any brick-and-mortar (i.e., physical) store areas. Retail data support a rich source for data mining.Retail data mining can help identify user buying behaviors, find user shopping ... Read More

Financial data collected in the banking and financial market are relatively done, reliable, and of huge quality, which supports systematic data analysis and data mining. Therefore it can present a few typical cases which are as follows −Design and construction of data warehouses for multidimensional data analysis and data mining − Data warehouses are required to be constructed for banking and financial records. Multidimensional data analysis methods must be used to analyze the general features of such data.For instance, one can like to view the debt and revenue changes by month, by region, by sector, and by several elements, along ... Read More

There are several popular text retrievals indexing techniques such as inverted indices and signature files.Inverted Index − An inverted index is an index structure that maintains two hash indexed or B+-tree indexed tables: document_table and term_table, where document_table consists of a set of document records, each including two fields: doc_id and posting_list, where posting_list is a list of methods (or pointers to methods) that appears in the document, arranged according to some relevance measure.term_table includes a set of term records, each including two fields: term_id and posting_list, where posting_list specifies a list of records identifiers in which the term occurs.It ... Read More