Data Mining techniques plays a vital role like extraction of required knowledge, finding unsuspected information to make strategic decision in a novel way which in term understandable by domain experts. A generalized frame work is proposed by considering non - domain experts during mining process for better understanding, making better decision and better finding new patters in case of selecting suitable data mining techniques based on the user profile by means of intelligent agents. KEYWORDS: Data Mining Techniques, Intelligent Agents, User Profile, Multidimensional Visualization, Knowledge Discovery.
Jean-Paul Benzeeri says, "Data Analysis is a tool for extracting the jewel of truth from the slurry of data. "And data mining and statistics are fields that work towards this goal. While they may overlap, they are two very different techniques that require different skills. Statistics form the core portion of data mining, which covers the entire process of data analysis. Statistics help in identifying patterns that further help identify differences between random noise and significant findings--providing a theory for estimating probabilities of predictions and more.
Data mining is the process of extracting useful information from an accumulation of data, often from a data warehouse or collection of linked datasets. Data mining tools include powerful statistical, mathematical, and analytics capabilities whose primary purpose is to sift through large sets of data to identify trends, patterns, and relationships to support informed decision-making and planning. Often associated with marketing department inquiries, data mining is seen by many executives as a way to help them better understand demand and to see the effect that changes in products, pricing, or promotion have on sales. But data mining has considerable benefit for other business areas as well. Engineers and designers can analyze the effectiveness of product changes and look for possible causes of product success or failure related to how, when, and where products are used.
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field. There is an urgent need for a new generation of computational theories and tools to assist humans in extracting useful information (knowledge) from the rapidly growing volumes of digital data.
By some estimates, 80% of an organization's data is unstructured content. This content includes web pages, call center transcripts, surveys, feedback forms, legal documents, forums, social media, and blog articles. Therefore, organizations must analyze not just transactional information but also textual content to gain insight and boost performance. A powerful way to analyze this textual content is by using text mining. Text mining typically applies machine learning techniques such as clustering, classification, association rules and predictive modeling.