Look Into Data Mining

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.

When consumers shop online, data mining is collecting their buying habits and other online searches. With every search they do, their information is collected by companies as Acxiom. These companies collect data such as age, race, sex, education

An example of how data mining is conducted and used to benefit business can be explained in the following scenario:

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— Data mining, also popularly known as Knowledge Discovery in Database, refers to extracting or “mining" knowledge from large amounts of data. Data mining techniques are used to operate on large volumes of data to discover hidden patterns and relationships helpful in decision making. Many application areas such as medical, research, stock market, weather forecasting, business strategies…etc data mining is very much helpful to gain the hidden and useful information. Nowadays the universities also have been started to use the data mining in-order to achieve highest quality in teaching. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in

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

Data mining has become a key technology for companies and researchers in many fields , The number and diversity of applications is growing over the years it is expected a significant increase in this growth and there are many commercial space worked on DM prematurely recently been applied DM in all areas in the banking, insurance, retail, telecommunications and pharmacy, health and government and all e-business types and many of domain (Figure 2 ) Data mining applications in 2008(http://www. kdnuggets. com).,The authors highlight the importance of developing a appropriate back testing environment that become the collection of Enough evidence to convince the end users that the system can be used in practice

Data mining is the process of discovering patterns, trends, correlations from large amounts of data stored electronically in repositories, using statistical methods, mathematical formulas, and pattern recognition technologies (Sharma n.d.). The main idea is to analyze data from different perspectives and discover useful trends, patterns and associations. As discussed in the previous chapter, the healthcare organizations are producing massive amounts of electronic medical records, which are impossible to process using traditional technologies (e.g., Microsoft excel). Therefore data mining is becoming very popular in this field as it can be used to identify the presence of chronic disease, detect the cause of the disease, analyze the effectiveness of treatment methods, predict different medical events, identify the side effects of the drugs, and so on. Kidney diseases such as CKD or AKI require immediate detection and medical attention based on the patient’s clinical condition, medical history, medication history and some demographical factors. From the literature survey, we discovered a good number of studies and tools that used data mining methods such as clustering, association, and classification to improve the decision-making ability of the healthcare providers regarding kidney disease. In the subsequence sections in this chapter, we present an overview of the data mining methods and discuss how they have been used in existing literature.

Data mining has become astronomically paramount for most of the business domains among them few of them are listed like marketing, financing and telecommunication. This has become possible because of the development of data base technology and systems in recent past few years. Data mining strategies is utilized for data processing. Operations performed on the data such as accumulation, utilize or administration is called data processing. A few real life demonstrations that can further demonstrate with the help of an example, a shop keeper requesting that client to fill in a counter slip for data process and to maintain the record for future [5]. Affiliation rule mining is a data mining strategy that can undoubtedly finds the patterns or association in astronomically quantity of facts units. So as for statistics to be valuable, it should have the following characteristics: Precise, Consummate, Malleable, Dependable, Pertinent, Simple, Timely Retrievable, and Verifiable.

Background - One of the most promising developments in the field of computing and computer memory over the past few decades has been the ability to bring tremendous complex and large data sets into database management that are both affordable and workable for many organizations. Improvement in computer power has also allowed for the field of artificial intelligence to evolve which also improves the sifting of massive amounts of information for appropriate use in business, military, governmental, and academic venues. Essentially, data mining is taking as much information as possible for a variety of databases, sifting it intelligently and coming up with usable information that will help with data prediction, customer service, what if scenarios, and extrapolating trends for population groups (Ye, 2003; Therling, 2009).

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

As computer and database technologies develop rapidly, data accumulates in a speed unmatchable by human capacity of data processing[2]. Data mining as a multidisciplinary joint effort from databases, machine learning and statistics, is championing in turning mountains of data into nuggets. Researchers and practitioners realize that in order to use data mining tools effectively, data processing is essential to successful data mining.PrimitiveThese are features which have an influence on the output and their role cannot be assumed by the rest.[1]

I am sure that use of this technology will grow radically in next few years.

Data mining is the process of extracting knowledge from large data sets. It uses artificial intelligence methods to discover the hidden relationships among the huge amount of data that is collected. It has a great potential to improve applications in many fields like Healthcare systems, Customer relationship management, Financial banking, Research analysis, Bio informatics, Marketing analysis, Education, Manufacturing engineering, Criminology and many more. Criminology is the study of crimes and typically a criminologist’s job include analyzing data to determine why the crime was committed and more importantly to predict and prevent criminal behavior in the future. It became an interesting field to apply data mining techniques because of its large datasets and the complexity of relationships between the data. This paper will discuss some of the tools and techniques used in this field to find out important information that will help and support the police forces and reduce social nuisance.

Computer-based technologies have lot of impact on the way we live, work, and play and socialize. The methods of analytics and datamining had changed over the past few decades. In 1970s datamining played an important role in scientific fields such as physics, biology, and climate science. In mid-1990s and into the first decade of the new millennium, data mining and analytics played important role in business practices.