Data Mining Information

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

Learning Outcome 6.7: Define data mining, and explain the three common forms for mining structured and unstructured data.

Data mining is essentially the ability to discover new information by exploring through various databases of existing information. According to Laura and Jack Cook, data mining "facilitates the discovery of previously unknown relationships among the data. …These operations present results that users already intuitively knew existed in the database."[2] As an example, let us take a school system consisting of three databases: one which stores the student profiles consisting of name and identification number, another to store student grades based on identification number, and the last one stores all the transactions at the bookstore through the student identification card. This is a simple example, but it should illustrate our point. Alone, the separate databases might not tell us much. With data mining techniques, the process might be able to tell us that in a particular school year, students of a certain ethnic background obtained above a 3.0 GPA, or that the bookstore sold mostly engineering books to students last year, or even that students who obtained above a 3.0 GPA were ones who bought engineering books. More specifically, the technology might be smart enough to associate that John Doe from Ireland had a 3.32 GPA in his engineering classes, even though he did not buy any engineering books from the bookstore. This type of technology is very powerful source of

Data analysis is a procedure of inspecting, cleaning, transforming, and modelling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. There are multiple facts and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains in data analysis. For data analysis we have to mine the data first for our purpose such that the data we can handle easily. Basically for data analysis our first thing to do our planning, how we are going to collect the data, our going data going to make sense or not, actually data will be meaningful for our object, after

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 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 class of database applications that looks for hidden patterns in a group of data that can be

18. _____ is the process of extraction of hidden predictive information from large data bases.

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

Although data mining is still in its infancy, companies in a wide range of industries –

Today with the ever growing use of computers in the world, information is constantly moving from one place to another. What is this information, who is it about, and who is using it will be discussed in the following paper. The collecting, interpreting, and determination of use of this information has come to be known as data mining. This term known as data mining has been around only for a short time but the actual collection of data has been happening for centuries. The following paragraph will give a brief description of this history of data collection.

Secondly, predictive analytics not only use statistical algorithms and forecasts on historical data but also anticipate likely situations to plan ahead. The means of these predictive models differs depending on their behaviour and future events but the aim is to go beyond reporting what has happened in providing the best assessment of what will happen. Data mining is an important component that tends to identify trends, patterns and relationship. They largely depend on statistical models and multivariate analysis techniques. Thereby decision making can be streamlined and new insights can be revealed leading to better decision making. Organisations are adapting to predictive analytics for competitive advantage. They are used in identifying trends, understanding customers, enhance business performance, predict behaviour and drive decision making. (SAS and Nyce)

Based on the assumption that history will repeat itself, there will be identifiable patterns of behaviour that can be used to predict future behaviour. This model is useful when you have a short time requirement (eg days) to analyse products in their growth stages to predict short-term outcomes.

Data Classification is the process of categorizing data into different groups or classes for effecting management. It is very essential for organization to classify data as it allows them to determine the degree of sensitivity and criticality of each class of data and as a result, appropriate level of protection can be applied based on the assigned sensitivity level. Over or under protection of data can be avoided with proper data classification as only the necessary level of