The Impact Of Technology On The Environment Development, Electric Devices, And Technology

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 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 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 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.

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,

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, a sub-branch of computer science, involving statistics, methods and calculations to find patterns in large amount of data sets, and database systems. Generally, data mining is the process to examine data from different aspects and summarizing it into meaningful information. Data mining techniques depict actions and future trends, allowing any individual to make better and knowledge-driven decisions.[1][2]

With the increased and widespread use of technologies, interest in data mining has increased rapidly. Companies are now utilized data mining techniques to exam their database looking for trends, relationships, and outcomes to enhance their overall operations and discover new patterns that may allow them to better serve their customers. Data mining provides numerous benefits to businesses, government, society as well as individual persons. However, like many technologies, there are negative things that caused by data mining such as invasion of privacy right. This paper tries to explore the advantages as well as the disadvantages of data mining. In addition, the ethical and global issues regarding the use of data mining

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

Generally, 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 increase revenue, cuts costs, or both. Data mining software 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.

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

Abstract— Data mining is a logical process that is used to search through large amount of data in order to find useful data [2].There are many different types of analysis that can be done in order to retrieve information from big data. Each type of analysis will have a different impact or result. Which type of data mining technique you should use really depends on the type of business problem that you are trying to solve.

Data mining is often referred to as “analytical intelligence” and is helping organizations for a better view of their business, to understand their customer needs and increase the effectiveness of the organization in the long run.

Data mining is the process of automatically discovering useful information from large data repositories [2]. The rapid advances in technologies led to accumulation of vast amount data. As the number of organizations grows day by day the breeding of new technologies and new styles of data also increases. It is difficult to handle them and store them in an efficient manner and of course retrieval of data is extremely challenging [3]. Nowadays we cannot use traditional methods to explore the data analysis because of the size of dataset. So it is very important to make study on data analysis. Most of the technologies are blended with data mining or we can say that data mining is vital and indispensible concept for every technology. Traditional machine learning algorithms like decision trees or artificial neural networks are examples of embedded approaches [9][10].Data mining tasks are mainly divided into predictive and descriptive. Predictive refers to predict the particular attribute based on other attributes. Descriptive task is to derive patterns like correlations, trends, clusters, trajectories and anomalies. Association analysis is used to discover patterns from correlated data and the output of analysis is represented using implication rules. Cluster analysis is the method of partitioning the datasets into different clusters and each cluster data is strongly correlated with intra-manner and inter clusters shows strong repulsion. There are several clustering methods but it