Data Mining is a Technique Used to Clarify and Classify Data

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.

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

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

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

Abstract - In the Data mining process, we can identify the patterns in the data that is hard to find using normal analysis. Several Mathematical and statistical algorithms are used in this approach to determine the probability of the event or scenario. The main aim of this process in terms of technical representation is to find the correlation amongst the attributes. There is a huge amount of discovery being carried out in this field creating a huge scope and jobs in this area. Several data mining algorithms are present that could determine different features present in the data that could lead in prediction and future analysis. Main Study report would consist of these algorithms that could help us predict and some sample data that we

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.

Data mining or Knowledge Discovery in Databases (KDD) is discovering patterns from large data groups through methods of artificial intelligence, machine learning ,statistics, and database systems. The aim of data mining process is to extract information from a data group and switch it to an ideal format for future . The data mining process comprise of database and data management aspects, data preprocessing, inference, complexity of discovered structures, and updating.

In today’s business world, information about the customer is a necessity for a businesses trying to maximize its profits. A new, and important, tool in gaining this knowledge is Data Mining. Data Mining is a set of automated procedures used to find previously unknown patterns and relationships in data. These patterns and relationships, once extracted, can be used to make valid predictions about the behavior of the customer.

Data mining works or performs these feats using a technique that called modeling. Modeling is simply the act of building model in one application where there is an answer and then we apply it to another situation that you don’t. This act of model building has been doing by people for a long time, certainly it before the advent

The proliferation, ubiquity and increasing power of computer technology has increased the volume of data oday`s mobile technologies and social media have collection and it`s storage manifold. This led to unleashed an exponential increase in information. continual growth in the size of data sets with Predictive analytics, a business intelligence technology consequent increase in complexity as well. Hands-on is one of the latest to take the future by storm with its data analysis is being increasingly augmented with immense potential for data- mining and efficacy. indirect, automated data processing Predictive analytics can be defined as any solution that techniquesclustered together and known as DATAIJERTsupports the identification of meaningful patterns and MINING.

This research paper is about the Comparative analysis of three data mining software’s selected based on four important criteria Performance, Functionality, Usability and Ancillary Tasks support. “Data Mining is a field of study that is gaining importance and is used to explore data in search of patterns or relationships between variables and is applied to new data used for predictions”. (Statistics – Textbook. (n.d.). Retrieved November 17, 2015). Selection of the appropriate data mining tools is critical to any research or business and this could impact the business in terms of money, resources and time. Data experts

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.

Since higher education has blurred the lines with traditional businesses, it is important to have the tools to assist them with valuable data and information, in making decisions. Using of data and having the right data mining tools can insure the institute’s success, in many forms, such as, identifying market trends, precision marketing, new products, performance management, grants and funding management, student life cycle management and procurement to mention a few. To get a better grasp on these benefits it’s important to understand data warehouse, data mining and the associated benefits.

Feature selection (FS) methods have been used in the since 70s, using in the fields of statistics and pattern recognition. Pattern recognition system is one of the most important and indispensable tasks in overcome the curse of dimensionality problem, which forms a motivation for using a suitable feature selection method. According to their working principles, there are two types of methods are using in feature selection: methods which select the best subset of features that has a certain number of features And methods which select the best subset of features according to their own principles, independent of outside size measures [base].

Based on these trends, large amount of data are being gathered and stored in databases, and data warehouses. The huge volume and fast pace made the power of data much stronger than what we expected, with lots of potential waiting us to maintain, explore and make decisions about. Using the efficient way to analyze the most helpful and valuable data, as well as to find out the hidden data is becoming urgent and important. Because of these needs, data mining started to be used as a helpful technology, and plays an important role under today’s studying and working environment.