Data Mining is a Technique Used to Clarify and Classify Data

Data mining is another concept closely associated with large databases such as clinical data repositories and data warehouses. However data mining like several other IT concepts means different things to different people. Health care application vendors may use the term data mining when referring to the user interface of the data warehouse or data repository. They may refer to the ability to drill down into data as data mining for example. However more precisely used data mining refers to a sophisticated analysis tool that automatically dis­ covers patterns among data in a data store. Data mining is an advanced form of decision support. Unlike passive query tools the data mining analysis tool does not require the user to pose individual specific questions to the database. Instead this tool is programmed to look for and extract patterns, trends and rules. True data mining is currently used in the business community for market­ ing and predictive analysis (Stair & Reynolds, 2012). This analytical data mining is however not currently widespread in the health care community.

Data mining software allows users to analyze large databases to solve business decision problems. Data mining is, in some ways, an extension of statistics, with a few

Data mining uses computer-based technology to evaluate data in a database and identify different trends. Effective data mining helps researchers predict economic trends and pinpoint sales prospects. Data mining is stored in data warehouses, which are sophisticated customer databases that allow managers to combine data from several different organization functions.

As stated above, data mining is often used to solve business decision problems, “it provides ways to quantitatively measure what business users should already know qualitatively” (Linoff, 2004). A growing number of industries are using data mining to become more competitive in their market by primarily focusing on the customers; increasing their customer relationships and increasing customer acquisition.

Today with the ever growing use of computers in the world, information is constantly moving from one place to another. What is this information, who is it about, and who is using it will be discussed in the following paper. The collecting, interpreting, and determination of use of this information has come to be known as data mining. This term known as data mining has been around only for a short time but the actual collection of data has been happening for centuries. The following paragraph will give a brief description of this history of data collection.

Abstract - In the Data mining process, we can identify the patterns in the data that is hard to find using normal analysis. Several Mathematical and statistical algorithms are used in this approach to determine the probability of the event or scenario. The main aim of this process in terms of technical representation is to find the correlation amongst the attributes. There is a huge amount of discovery being carried out in this field creating a huge scope and jobs in this area. Several data mining algorithms are present that could determine different features present in the data that could lead in prediction and future analysis. Main Study report would consist of these algorithms that could help us predict and some sample data that we

In its infancy, data mining was as limited as the hardware being used. Large amounts of data were difficult to analyze because the hardware simply could not handle it [1]. The term "data mining" first began appearing in the 1980 's largely within the research and computer science communities. In the 1990 's it was considered a subset of a process called Knowledge Discovery in Databases of KKD [1]. KKD analyzes data in the search for patterns that may not normally be recognized with the naked eye. Today however, data mining does not limit itself to databases,

Data mining works or performs these feats using a technique that called modeling. Modeling is simply the act of building model in one application where there is an answer and then we apply it to another situation that you don’t. This act of model building has been doing by people for a long time, certainly it before the advent

Many other terms are being used to interpret data mining, such as knowledge mining from databases, knowledge extraction, data analysis, and data archaeology. Data mining is one of the provoking and significant areas of research. Data mining is implicit and non-trivial task of identifying the viable, novel, inherently efficient and perspicuous patterns of data. Figure 1 represents the data mining as part of KDD process. The hidden relationships and trends are not precisely distinct from reviewing the data. Data mining is a multi-level process involves extracting the data by retrieving and assembling them, data mining algorithms, evaluate the results and capture them. Data Mining is also revealed as necessary process where bright methods are used to extract the data patterns by passing through miscellaneous data mining

Feature selection (FS) methods have been used in the since 70s, using in the fields of statistics and pattern recognition. Pattern recognition system is one of the most important and indispensable tasks in overcome the curse of dimensionality problem, which forms a motivation for using a suitable feature selection method. According to their working principles, there are two types of methods are using in feature selection: methods which select the best subset of features that has a certain number of features And methods which select the best subset of features according to their own principles, independent of outside size measures [base].

The Data mining have many techniques for extracting dataset like Clustering, Classification, Regression, and Association Rule Learning. The clustering technique is the task of discovering structures in homogeneous data to be in one group, there the

In today’s business world, information about the customer is a necessity for a businesses trying to maximize its profits. A new, and important, tool in gaining this knowledge is Data Mining. Data Mining is a set of automated procedures used to find previously unknown patterns and relationships in data. These patterns and relationships, once extracted, can be used to make valid predictions about the behavior of the customer.

This research paper is about the Comparative analysis of three data mining software’s selected based on four important criteria Performance, Functionality, Usability and Ancillary Tasks support. “Data Mining is a field of study that is gaining importance and is used to explore data in search of patterns or relationships between variables and is applied to new data used for predictions”. (Statistics – Textbook. (n.d.). Retrieved November 17, 2015). Selection of the appropriate data mining tools is critical to any research or business and this could impact the business in terms of money, resources and time. Data experts

Based on these trends, large amount of data are being gathered and stored in databases, and data warehouses. The huge volume and fast pace made the power of data much stronger than what we expected, with lots of potential waiting us to maintain, explore and make decisions about. Using the efficient way to analyze the most helpful and valuable data, as well as to find out the hidden data is becoming urgent and important. Because of these needs, data mining started to be used as a helpful technology, and plays an important role under today’s studying and working environment.

The proliferation, ubiquity and increasing power of computer technology has increased the volume of data oday`s mobile technologies and social media have collection and it`s storage manifold. This led to unleashed an exponential increase in information. continual growth in the size of data sets with Predictive analytics, a business intelligence technology consequent increase in complexity as well. Hands-on is one of the latest to take the future by storm with its data analysis is being increasingly augmented with immense potential for data- mining and efficacy. indirect, automated data processing Predictive analytics can be defined as any solution that techniquesclustered together and known as DATAIJERTsupports the identification of meaningful patterns and MINING.