Importance Of Association Rule Mining

1.1. DATA MINING Data mining refers to extracting or mining knowledge from large amounts of data. Data mining has attracted a great deal of attention in the information industry and in society as a whole in recent years, due to the wide availability of huge amounts of data and the forthcoming need for turning such data into useful information and knowledge. The information and knowledge gained can be used for applications ranging from market analysis, fraud detection, and customer retention, to production control and science exploration. Data mining can be viewed as a result of the natural evolution of information technology.

Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. B.2 Introduction The growing popularity and development of data mining technologies bring serious threat to the security of individual's

The extensive use of information and communication technology has generated large volumes of data storage. The data repositories might contain massive amount of useful information. In order to extract useful knowledge from these data repositories for making better decision, necessitate the need for proper methods of extracting knowledge. Machine learning is an important technique which extracts necessary knodledge and information such as association, patterns, changes and anomalies from various data repositories (Barka et al., 2010). The idea of machine learning is something resulting from this environment.

Data Science vs Statistics Data science is one of the rapidly emerging trends in computing and is a vast multi-disciplinary area. Data science combines the application of subjects namely computer science, software engineering, mathematics and statistics, programming, economics, and business management. Data science is based on the collection, preparation, analysis, management, visualization and storage of large volumes of information. Data science in simple terms can be understood as having strong connections with databases including big data and computer science. A data scientist is an individual with adequate domain knowledge relevant to the question addressed.

DOCUMENTATION A concise and accessible documentation is essential for the management of the collections, research and public services. The process of documentation includes registration, inventory and cataloging, and the use of manual and electronic formats to access to information according to established standards. “There are a number of software packages available which are suitable for producing inventories. Such databases are powerful tools designed to handle large amounts of information” (Xavier-Rowe 2010, p.3). A complete inventory of the collection is fundamental.

Practitioners who carry out fault tree analysis must have the knowledge and experience on the ERP system; involvement and inputs from key stakeholders are essential as well. Considering the complexity of ERP system implementation and the high stakes to the hosting organisation, it is important that consensus on these decisions is reached not only within the ERP project team, but also with key stakeholders outside of the team such as senior management and leading end-users. In summary, with a focus on methodological development that will be followed by further case studies on practical application, this research proposes a probabilistic risk assessment approach based on fault tree analysis that aims to address ERP system usage failure. It is an effort to introduce probabilistic risk assessment techniques into the domain of information system risk management. The approach models the risk relationship between ERP system usage failure, ERP

To what extent is it ethical to collect customer’s data and pattern trends for the benefit of the supermarket business Q23. Discuss the best possible solutions to analyze data from customer’s purchases. ( Q24. Outline on the problems ASI is having. Q25.

Abstract Big data is everywhere. Big data revolution is creating paths to collect and analyze information of varying sizes, types and volume. It’s not only used in sectors like marketing, sales and product development. The potential use of big data is also spread to HR and Finance which help in finding new insights and strategic decision making. With big data, HR has exceptional opportunities to become more data driven analytical and strategic in the way it obtains talent.

A. Group Assignment a. Discuss the two data mining methodologies The process of going through massive sets of data looking out for unsuspected patterns which can provide us with advantageous information is known as data mining. With data mining, it is more than possible or helping us predict future events or even group populations of people into similar characteristics. Cross Industry Standard Process for Data Mining (CRISP-DM) is a 6-phase model of the entire data mining process which is commonly used across industries for a wide array of data mining projects and provides a structured approach to planning a data mining project.

There are many definitions of GIS. GIS are often described as an organized collection of computer hardware, software, geographical data and personnel designed to efficiently capture, store, update, manipulate, analyze and display all forms of geographically referenced information. Geographical information systems and maps are valuable in strengthening the whole process of epidemiological surveillance information management and analyses. A GIS provides an excellent means of collecting, updating and managing epidemiological surveillance and related information. It can store, handle and geographically integrate large amounts of information from different sources, programs and sectors.