Data Mining And Evolutionary Algorithms For Multi Objective Optimization Problems

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.

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 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 preparation is one of the most significant and time-consuming phases of data mining projects (Steinbach et al., 2005; Han et al., 2006; Yau, 2011). The data needs to be prepared to be in an appropriate state for analysis, maintaining its representativeness of the real world but in a format that is appropriate for the data analysis tools. Therefore, data pre-processing techniques like data selection, data cleaning, constructing new data, integrating data and transformation of data were used in this case study.

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

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.

Data Mining is considered as the effective knowledge process to perform the data value analysis so that effective data discovery over the application data can be performed. These kind of da mining operations are defined a low level

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.

it is the most effective data mining technique to discover hidden or desired pattern among the large amount of data. It is responsible to find correlation relationships among different data attributes in a large set of items in a database .

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 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

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

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.

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.