Data mining

Knowledge Discovery and Data Mining are rapidly evolving areas of research that are at intersection of multiple application areas and approaches. Today no field either it belongs to computer or not, knowledge discovery is required. The loss prediction, cost estimation, identification of market moves are the common application areas where knowledge discovery is essential. Knowledge discovery is not an individual process, instead it is the combination of various session data operations that are applied

Rule induction is one of the major forms of data mining and is perhaps the most common form of knowledge discovery in unsupervised learning systems. It is also perhaps the form of data mining that most closely resembles the process that most people think about when they think about data mining, namely “mining” for gold through a vast database. The gold in this case would be a rule that is interesting - that tells you something about your database that you didn’t already know and probably weren’t

AT CHARLOTTE STUDY REPORT ON Data Mining Submitted By Submitted To Harshil Sheth DR. Anthony B. Wilkinson 800833329 Submitted in the partial fulfilment of Master’s Degree in Computer Science Contents 1. Abstract 2. Need for Data Mining 3. History of Data Mining 4. DATA MINING PROCESS 5. Applications of Data Mining 6. Privacy Concerns and Ethics 7. Precaution to be taken before using the data 8. References Abstract Data mining is the analysis step of the "Knowledge

TERM PAPER FOR OPERATING SYSTEMS DATA WAREHOUSES, DECISION SUPPORT AND DATA MINING Date: 09/11/2011 “I certify that the work contained in this paper is wholly mine. This paper has not been used to meet requirements in another course. It has not been purchased nor written by someone else, nor written for me. Exceptions to the aforementioned constitute plagiarism and an honor and ethics violation and therefore will result in a course grade of F and any other University remedies as appropriate

Privacy Issues in Data mining and Data Publishing: With the buzz around many big data applications, privacy concerns regarding their uses have also grown. With the personal data has been mined and published every day, the battle to reclaim the privacy starts vigorously. E-commerce websites harvests information about all the online searches of customers. Social Media exposes the likes and preferences of people, their photos and all their daily activities. Video surveillance monitors the movement of

Look at Data Mining in the Pharmaceutical Industry Topics Covered: 1) What is Data Mining and why is it used? 2) How is Data Mining used in the Pharmaceutical Industry? 3) Recent debate in the legality of Data Mining and the Pharmaceutical Industry Pharmaceutical companies are taking advantage of the growing use of technology in the healthcare arena by using data to enhance their marketing efforts and increase the quality of research and development. The process of data mining allows

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

Data Mining Techniques and Their Applications Deepika Sattu, 800721246, dsattu@uncc.edu 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

Data miming 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. The Data mining

manage large volumes of business data. The use of database systems in supporting applications that employ query based report generation continues to be the main traditional use of this technology. However, the size and volume of data being managed raises new and interesting issues. Can we utilize methods wherein the data can help businesses achieve competitive advantage, can the data be used to model underlying business processes, and can we gain insights from the data to help improve business processes