Data Mining: Concepts and Techniques

With progression in technology specifically in last three decades or so, an enormous magnitude of information has been transitioned into a digital form, which resulted in formation of enormous data repositories. With accrual of information in these repositories a challenge persisted as how to extract meaningful knowledge from it. Data mining as a tool was used to tackle the situation. Data mining considered as stepping stone to procedure of knowledge discovery in databases, this is a procedure of extracting hidden information from enormous sets of databases to excavate eloquent patterns and rules. Data mining has now become an indispensable component in almost every field of human life. The present article provides an analysis of the available literature on data mining. The concept of data mining as well as its various methodologies are summarized. Some applications, tasks and issues related to it have also been illustrated.

Sigmod Record, 2002

Mining information from data: A presentday gold rush. Data Mining is a multidisciplinary field which supports knowledge workers who try to extract information in our "data rich, information poor" environment. Its name stems from the idea of mining knowledge from large amounts of data. The tools it provides assist us in the discovery of relevant information through a wide range of data analysis techniques. Any method used to extract patterns from a given data source is considered to be a data mining technique.

—With an enormous increase in data stored in data warehouses and databases, it is important to develop tools which could analyze data and refine required knowledge from it. This paper provides an overview of data mining and how to extract a basic knowledge. This paper also gives review about various applications where data mining can be effective to improve their business and get excellent results. Even we have discussed some future trends where data mining can be proved effective in various fields.

Data mining may be a process of distinguishing and extracting hidden patterns and knowledge from databases and data warehouses. It is also referred to as knowledge Discovery in Databases (KDD) and permits knowledge discovery, data analysis, and data visualization of large databases at a high level of abstraction, while not a selected premise in mind. The operation of data mining is known by employing a technique known as modeling with it to create predictions. There are various algorithms and tools on the market for this purpose. Data mining encompasses a large variety of applications ranging from business to medication to engineering. This paper provides a survey of data mining technology, its models, and task, applications, major problems, and directions for advance analysis of data mining applications. https://sites.google.com/site/ijcsis/vol-11-no-5-may-2013

Knowledge Discovery in Databases is the process of searching for hidden knowledge in the massive amounts of data that we are technically capable of generating and storing. Data, in its raw form, is simply a collection of elements, from which little knowledge can be gleaned. With the development of data discovery techniques the value of the data is significantly improved. A variety of methods are available to assist in extracting patterns that when interpreted provide valuable, possibly previously unknown, insight into the stored data. This information can be predictive or descriptive in nature. Data mining, the pattern extraction phase of KDD, can take on many forms, the choice dependent on the desired results. KDD is a multi-step process that facilitates the conversion of data to useful information.Our increased ability to gain information from stored data raises the ethical dilemma of how the information should be treated and safeguarded.

This paper addresses the discussion of impact of data mining in today's fast growing world. Data mining means the extraction of hidden predictive information from large databases. It is an interactive information discovery process that includes data acquisition, data integration, data exploration, model building, and model validation. This paper provides an overview of data mining process, some well known techniques, software and few application areas.

The Knowledge Engineering Review, 2011

Data mining has become a well established discipline within the domain of Artificial Intelligence (AI) and Knowledge Engineering (KE). It has its roots in machine learning and statistics, but encompasses other areas of computer science. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large scale data mining to be conducted. Unlike other innovations in AI and KE, data mining can be argued to be an application rather then a technology and thus can be expected to remain topical for the foreseeable future. This paper presents a brief review of the history of data mining, up to the present day, and some insights into future directions.

2005

Abstract Data Mining (DM) has enjoyed great popularity in recent years, with advances in both research and commercialization. The first generation of DM research and development has yielded several commercially available systems, both stand-alone and integrated with database systems; produced scalable versions of algorithms for many classical DM problems; and introduced novel pattern discovery problems.

Data mining, the extraction of hidden predictive information from large databases, invented as a powerful new technology with great potential to help companies and organizations to focus on the most important information in their data warehouses. Data mining uses machine learning, statistical and visualization techniques to discovery and present knowledge in a form which is easily comprehensible to humans. Various popular data mining tools and techniques are available today for supporting large amount of applications. Data mining tools predict future trends and behaviors, allowing it’s users to make proactive, knowledge-driven decisions. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. With these large amount of features and applications there are some challenging issues also which are not exclusive and are not ordered in any way. This paper presents an overview of the data mining technique, Some of its vital applications and issues needs to be addressed.

This paper presents a first step towards a unifying framework for Knowledge Discovery in Databases. We describe finks between data milfing, knowledge discovery, and other related fields. We then define the KDD process and basic data mining algorithms, discuss application issues and conclude with an analysis of challenges facing practitioners in the field.