Classification Techniques Analysis

Data mining is the discovery of knowledge and useful information from the large amounts of data stored in databases. It is referred to as knowledge discovery from databases (KDD), is the automated or convenient extraction of patterns representing knowledge implicitly stored in large databases. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time consuming to resolve. Classification techniques are widely used in data mining to classify data among various classes. Classification techniques are being used in different industry to easily identify the type and group to which a particular tuple belongs. In this paper, different classification techniques are summarized. These techniques are applied on XML data to analyze their advantages and disadvantages. Information coded in XML is easy to read and understand, plus it can be processed easily by computers. It’s open and extendible i.e. there are no fixed set of tags. New tags can be created as they are needed. It contains machine-readable structured information. This is a major advantage over HTML or plain text. XML is self-descriptive and XML documents can be stored without such definitions, because they contain metadata in the form of tags and attributes. It separates content from presentation. Keywords— Data mining, classification, XML data, Support vector machines.