Data Mining And Evolutionary Algorithms For Multi Objective Optimization Problems

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

The decision support and intelligent systems that can be used in the company include enterprise-wide systems, knowledge work systems, and intelligent techniques, Great World Enterprises will focus on intelligent techniques for the decision support. It is a database technology that will allow the firm to capture data and analyze the resulting patterns (Aronson, Liang, & Turban, 2005). Data mining will be at the center of the decision support and intelligence systems. It is important to note that data mining is the process used by organizations to sort large data sets so as to identify patterns and determine relationships. The process will begin with the construction of a data warehouse. It is a relational database system that will enable Great World Enterprises to store large quantities of structured and unstructured data. The data warehouse system will include a business intelligence section that will process the stored data to establish patterns and relationships. The analysis will be

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.

Data Mining is a computer based-process for converting large data volumes to information and knowledge by finding patterns within the data using different techniques. It is sorting through data to identify patterns and establish relationships. Data mining helps resolving problems that are time consuming when traditional techniques are used. Data mining techniques are used to predict future trends and to make wise decisions. There are multiple Data Mining techniques available to the Data diggers to make their life easy. In my study report I will be discussing about the different mining techniques, advantages and disadvantages and also about a use case of the data mining techniques on shark attack dataset to predict the attack of sharks based on various attributes.

The exponentially increasing amounts of data being generated each year make getting useful information from that data more and more critical. The information frequently is stored in a data warehouse, a repository of data gathered from various sources, including corporate databases, summarized information from internal systems, and data from external sources. Analysis of the data includes simple query and reporting, statistical analysis, more complex multidimensional analysis, and data mining. Data analysis and data mining are a subset of business intelligence (BI), which also incorporates data warehousing, database management systems, and Online.

Data mining is the procedure of getting new patterns from large amount of data. Data mining is a procedure of finding of beneficial information and patterns from huge data. It is also called as knowledge discovery method, knowledge mining from data, knowledge extraction or data/ pattern analysis. The main goal from data mining is to get patterns that were already unknown. The useful of these patterns are found they can be used to make certain decisions for development of their businesses. Data mining aims to discover implicit, already unknown, and potentially useful information that is embedded in data.

As with the development of the IT technologies , the amount of cumulative data is also Growing. It has resulted large amount of data stock in databases therefore the Data mining comes into model to explore and analyses the databases to extract the interesting and previously obscure patterns and rules well-known as association rule mining It was first introduced in 1993.

Data mining is a new technology which could be used in extracting valuable information from data warehouses and databases of companies and governments. It involves the extraction of hidden information from some raw data. It helps in detecting inconsistency in data and predicting future patterns and attitude in a highly proficient way. Data mining is implemented using various algorithm and framework, and the automated analysis provided by this algorithm and framework go ahead of evaluation in dataset to providing solid evidences that human experts would not have been able to detect due to the fact that they

Data Mining is the non-trivial extraction of potentially 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. There are various research domains in data mining specifically text mining, web mining, image mining, sequence mining, process mining, graph mining, etc. 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

Data mining and knowledge discovery is the name frequently used to refer to a very interdisciplinary field, which consists of using methods of several research areas to extract knowledge from real-world datasets. There is a distinction between the terms data mining and knowledge discovery which seems to have been introduced by [Fayyad et al.1996].the term data mining refers to the core step of a broader process, called knowledge discovery in database. Architecture of data mining structure is defined the following figure.

Data mining is the process of extracting useful knowledge from large databases or data warehouses. It can be also said as a set of mathematical functions and data manipulation techniques to extract useful data from databases. Data mining can also be said as knowledge discovery process in other words. It explores a large collection of data into a meaningful patterns and rules based on the queries provided by users using data mining query language. The meaningful patterns and rules are generated by analysing the database. Data mining makes use several techniques such as clustering, classification, association rule mining and so on to generate the meaningful patterns from the databases. The purpose of this report is to describe how data are prepared for data

data is also Growing. It has resulted large amount of data stock in databases , depot and other repositories . therefore the Data mining comes into model to explore and analyses the databases to extract the interesting and previously obscure patterns and rules well-known as association rule mining

Our research is to apply DM on a given data set extracted from data held in RMIS at JKUAT. The literature review on the methodology used is presented in this chapter under Section 2.4. Before this we have the definition of terms in DM given in section 2.2 defining data mining, concept of knowledge

The Data mining it also be known as that the way of picking the data and from big mix of Information from the cloud. And it can also be say’s like it’s a data mining is digging or extracting knowledge from the data.