What Are The Pros And Cons Of Data Mining

To begin with, Dell software an information technology enterprises describes Data Mining as “an analytic process designed to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the

Data mining is the practice of gathering data from various sources and manipulating it to provide richer information than any of contributing sources is able to do alone or to produce previously unknown information. Businesses and governments share information that they have collected with the purpose of cross-referencing it to find out more information about the people tracked in their databases.

Data Mining. It is the process of discovering interesting knowledge that are gathered and significant structures from large amounts of data stored in data warehouse or other information storage.

Data Mining is an analytical process that primarily involves searching through vast amounts of data to spot useful, but initially undiscovered, patterns. The data mining process typically involves three major steps—exploration, model building and validation and finally, deployment.

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.

What is data mining? Data mining is the deriving new information from massive amounts of data in databases (Sauter, 2014, p. 148). Chowdhurry argues that data mining is part of KDD. KDD is knowledge discovery in databases, it is a process that includes data mining. In addition to data mining, KDD includes data preparation, modeling and evaluation of KDD. KDD is at the heart of this research field. This research field is multidisciplinary and includes data visualization, machine learning, database technology, expert systems and statistics. Overall, the use of a case based reasoning and data mining tools within an information system would create a CBR system to solve new problems with adapted solutions and could be used in many industries such as education and healthcare (Chowdhurry,

Knowledge attained wth the use of data mining techniques can be used to make innovative and successful decisions that will increase the success rate of health care sector and the health of patients. In this paper, the study of classification algorithms in data mining techniques and its applications are discussed. The popular classification algorithms used in healthcare domain are explained in detail. The open source data mining tools are discussed. The applications of healthcare sector using data mining techniques are studied. With the future development of information communication technologies, data mining will attain its full potential in the discovery of knowledge hidden in the health care organizations and medical

Data mining for healthcare is useful in evaluating the effectiveness of medical treatments and it is interdisciplinary field of study that has its roots in databases statistics machine learning and data visualization. Diabetic disease refers to the heart disease that develops in persons with diabetes. The term diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin. The cardiovascular disease is class of diseases that involves the heart or blood vessels Even though many data mining classification techniques exist for the prediction of heart disease there is insufficient data for the prediction of heart diseases in a diabetic individual. The main objective focus on this research is to find an optimal

Data mining as it has been established is simply a way of getting hidden knowledge or information from data, the technique(s) employed search for concurrency, relationships, and outliers in this data that they present as knowledge [4]. This knowledge can then be used in different applications. Prediction is one of the ways that the hidden knowledge gotten from the data can be used. The main aim is to use a large number of past values to consider probable future [9]. Heart disease is the common term for a variety of diseases, conditions, and disorders that affect the heart and the blood vessels [6]. Symptoms of heart disease vary depending on the type of heart disease. Congenital heart disease suggests to a problem with the heart 's structure and function due to abnormal heart development before birth. Congestive heart failure is when the heart does not pump adequate blood to the other organs in the body [8]. Coronary heart disease or in its medical term Ischemic heart disease is the most frequent type of heart problem [24]. Coronary heart disease is a term that refers to damage to the heart that happens because its blood supply is decreased; it leads to fatty deposits build up on the linings of the blood vessels that provide the heart muscles with blood, resulting in heart failure. Heart disease prediction using data mining is one of the most interesting and challenging tasks. Shortage of specialists and high wrongly diagnosed

Data mining is the process of analysing data to discover meaningful patterns within the data resulting in extracting useful information that may have not been discovered yet. Data mining borrows techniques from a variety of fields such as statistics, machine learning and artificial intelligence. Because of its usefulness, data mining has been used in a range of industries such as, banking, telecommunications, retail, marketing, and insurance.

Data mining is when a financial analyst gathers consumer information and looks for patterns that a business can exploit. A simplified data mining example is when a restaurant manager knows the local yearly convention schedule based on experience. The manager can cross-reference that information with historical sales results to predict such things as forecasted profit or labor demand. With this information, the manager can estimate an advertising budget or hire temporary staff to handle anticipated work load. When medium to large-sized businesses use data mining, they uncovering these same information points; however, revenue gains can range from millions to billions of dollars. There are several techniques that firms frequently employ to find gold in information.

From my understanding, data mining is a series of operation to dig up a value-added process from a bunch of data in the form of knowledge that is not known for manually. Knowledge discovery in database is a term that we called for data mining in science computer. Data mining also about to find a new information in a lot of data. Not only that, data mining is searching for patterns or relationships in one or more databases and it way to generate new information. Besides that, for secondary use, the information collected for one purposed used for another purpose and the information about customers is a valuable commodity. But, does we know how the data mining is work?

Across a wide variety of fields, data emanating from the massive healthcare insurance providers such as government and private companies in healthcare are being collected and stored at tremendous pace. Thus, there is a need felt by most of the companies to manage their wealth of knowledge. Hence, due to the tremendous increase in data, extracting useful information from that data became important. Thus, to extract useful information from the database, Knowledge Discovery in the Database (KDD) is needed. Therefore, KDD is defined as a process of identifying valuable, important, useful and understandable patterns from a large complex database (Maimom & Rokach, 2007).

Abstract— Data Mining extracts useful information about data. In other words, Data Mining extracts the knowledge or interesting information from large set of structured data that are from different sources. Data mining applications are used in a range of areas such as it is used for financial data analysis, retail and telecommunication industries, banking, health care and medicine. In health care, the data mining is mainly used for disease prediction. In data mining, there are several techniques have been developed and used for predicting the diseases that includes data preprocessing, classification, clustering, association rules and sequential patterns. This paper analyses the performance of two classification techniques such as Bayesian

Data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to gain competitive advantage, improve processes, gain efficiency, save costs, utilizing and allocating resources optimally.