An Extraction Of Knowledge Using Meta Heuristic Models Essay

Data mining is a very important component in today’s big data [22, 23]. Data mining is essential for everyone from large businesses to government organizations. It helps to identify trends, patterns and make predictions by exploring, comparing, researching and analyzing data.

DATA MINING: means searching and analyzing large masses of data to discover patterns and develop new information.

and analyzing enormous sets of data and then extracting the meaning of the data. Data mining

Data mining is used to uncover hidden knowledge and patterns from a large amount of data. In finance, there is enormous data which generates during business operations and trading activities. Extracting valuable data from them manually might be unable or spend a lot of time. As a result, data mining plays an importance role of discovering data that can be used to increase profits and evaluate risks of strategic planning and investment. The data mining methods in finance are derived

Data mining is finding the routines and examples in large databases to guide choices about future exercises. It is normal that data mining tools to get the model with negligible information from the client to identify. Data mining is the utilization of automated data analysis techniques to discover already undetected connections among data things. It regularly determines the

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 prediction model works on the process of identifying the patterns based on the historical information to predict the new incoming data sets. This prediction modelling is much useful in the case of decision making process in the business models. On the other way, Descriptive model describes the data in an efficient way by means of grouping the data by using clustering; association rules principles of data mining.

Data Mining technique is the result of a long process of studies and research in the area of databases and product development. This evolution began when business data and companies was stored for the first time on computer device, with continuous improvements in access to data and more newly, produced technologies that allow users to navigate during their data in real time. Data mining is a approach that help to mine important data from a large database. It is the technique of classification during huge amounts of data and chosen out relevant information during the use of certain advanced algorithms. Like more data is collected, with the amount of data doubling every one years, data mining is becoming an more and more important tool to convert this data into information. Data mining takes this evolutionary process behind retrospective data access and navigation to prospective and proactive information delivery. Data mining is very useful and ready in applications in the business

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

This technical paper consists of the study of data mining algorithm in cloud computing. Cloud Computing is an environment created in user’s machine from online application stored in clouds and run through web browser. Therefore, it is essential to manage user’s data efficiently. Data mining also known as knowledge discovery is the process of analyzing data from different perspectives and summarizing it into useful information where the information can be used to increase revenue, cut costs of implementation and maintenances, or all. Data mining software and/or algorithms is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. The process of mining data can be done in many ways; this paper discusses the theoretical study of two algorithms K-means and Apriori, their explanation using flow chart and pseudo code, and comparison for time and space complexity of the two for the dataset of an “Online Retail Shop”.

Abstract— Data mining is logical process that is used to extract or “mining” large amount of data in order to find useful data [2]. Knowledge discovery from Data or KDD is synonym for Data Mining[13].There are many different types of techniques that can be used to retrieve information from large amount of data. Each type of technique will generate different results. The type of data mining technique that should be selected depends on the type of business problem that we are trying to solve.

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 (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. Data mining tools not only analyze data from all perspectives, but also form relationships between seemingly random data into meaningful and actual information by finding correlations or patterns among dozens of fields in large relational databases which can be used to improve business and also gain intelligence which safeguard against

In every day live, the word ‘Mining’ refer to the process that discovered a small set of valuable pieces from a great deal of raw material as in mining process of gold from rocks or sand. According to [3] Data Mining, or Knowledge Discovery in Databases (KDD) as it is also known, is the process of extraction of implicit information that previously unknown and potentially useful from database. By using a number of different technical, such as clustering, data summarization, learning classification, finding dependency networks, analyzing changes, and detecting anomalies. Data Mining refers to a variety of techniques that can be used to analyses and observes database in order to find relationships or summarize the data in ways that can be put to use in different areas such as decision making, prediction and estimation and to do that there are a sequence of the process [2] . As show in figure (1.1)