Final Exam Part 2
Bharat Surana Rajender Kumar Surana
Sullivan University
CSC550X - Data Mining December 08, 2017
1.
Data mining refers to mining required data from large data warehouses and repositories by looking for relevant patterns, changes, anomalies, associations and significant structures. Data mining has been known as an essential step of knowledge discovery (What is Data Mining?). It is essential to extract the right data in order to employ it into different applications as per its need.
The process of data mining involves several steps and each of this step has its own significance. Data integration, Data cleaning, Data transformation, Data mining, Data selection, Pattern evaluation and Knowledge presentation are the major steps
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This step is crucial since failure in data cleaning can result in highly misleading results (Rudo, 2013). The next step in data mining process is data integration which includes integrating multiple, heterogeneous data sources into one in order to ease the data usage process. After Data integration comes Data selection where only relevant data required for analysis is retrieved from the database. Once Data integration is completed, the data undergoes consolidation into forms so that a set of relevant data appears as a summary. This process is called Data transformation.
The next two steps involved are Data mining and Pattern Evaluation wherein certain algorithms are used to identify and extract particular patterns of data which represent the knowledge. The last step involved in data mining is knowledge presentation where the user is presented with an output of the mined knowledge using certain visualization
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One-node decision tree is one classic example discussed among all other example in the paper. The first check of this one-node decision tree model is to verify that the records are assigned to different classes and not to one class. We came across many data mining processes that had inadequate models during the course of study (Berry & Linoff, 1999). For example, in classification of clothing stores between customers using associate rules. One of the drawbacks of using association rules is the number of rules that are produced which increases the number of iterations in a data mining model (Berry & Linoff,
Chet Craig is the Central Plant Manager of the Norris Company. He started as an expediter in the company's eastern plant and was quickly promoted to Production Supervisor in three years. After two years, he was promoted to Assistant to the Manager of the Eastern Plant. Five years later, Chet was transferred to the central plant as an Assistant, and after one month, was promoted to his current position.
The Human and Ethical Aspects of Big Data, quickly outlines an array of ethical problems that big data has had and will continue to develop in the upcoming years in our society. The author begins by giving an example of the earliest use of big data, the census. Census data can provide a plethora of benefits to society, representation in government, civil planning, such as road and water treatment, as these take years to construct and having the population and these necessities, Time align is crucial. As the author, uses the example of the internment of Japanese-Americans in 1943. Here is big-data supposed to help the citizens of the Unites States, the big data that is supposed to be private. Now in 1943, big data being used to discriminate
Data aggregation is a type of data mining and/or processing of data that is not meaningful in its form, where it can be transformed into meaningful information that can be related to a specific person or thing. Another definition of data aggregation is “data aggregation is a type of data and information mining process where data is searched, gathered and presented in a report-based, summarized format to achieve specific business objectives or processes and/or conduct human analysis.” Data aggregation is a double sword edge that can be devoted for good or bad proposes. After attack of September 11,2001 a private company that owned by Hank Asher, has developed a complex algorithm that search and s...
K. The first step in this approach is step K, which is defined as accessing what I know. In the first part of this step, the teacher will write the topic on the board and the students brainstorm what they know about it.
In recent years, the efforts of clusters are subject to a strong academic and political enthusiasm. The term "cluster" is fashionable and attractive concept. Indeed, in a context of intense competition between countries and regions, there is a questioning of the competitive positioning of territories that have to adapt to a carrier opportunities and uncertainties of globalization. For keen to strengthen the potential of their regions governments, clusters bring solutions sought: company mergers and resulting benefits appear as drivers of innovation and competitiveness of regional economies.
The four key processes in the data quality management model are analysis, warehousing, collection and application of data (AHIMA 2)
I think that the best and most effective way to implement the process would be to add in past information or data in the system, doing this will help because when old information is keyed into the system, it would help the system get used to the data that is being input into the system. This will definitely make new and upcoming data being added to the system becomes faster, accurate and efficient and also the work will be easier for the ...
There are various kinds of definitions about what data mining is. The authors in [1] define data mining as “the process of extracting previously unknown information from (usually large quantities of) data, which can, in the right context, lead to knowledge”. Data mining is widely used in areas such as business analysis, bioinformatics analysis, medical analysis, etc. Data mining techniques bring us a lot of benefits. Business companies can use data mining tools to search potential customers and increase their profits; medical diagnosis can use data mining to predict potential disease. Although the term “data mining” itself is neutral and has no ethical implications, it is often related to the analysis of information associated with individuals. “The ethical dilemmas arise when data mining is executed over the data of an individual” [2]. For example, using a user’s data to do data mining and classifying the user into some group may result in a variety of ethical issues. In this paper, we deal with two kinds of ethical issues caused by data mining techniques: informational privacy issues in web-data mining and database security issues in data mining. We also look at these ethical issues in a societal level and a global level.
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
The main aim of any industry today is to pay attention to the multiple opportunities existing for improvements. The links of the mining industry to its primary resources multiplies with its functions whereas in other industries, there are layers of processes between the primary resources and the final product (McKay, 2009). As described by Carroll & Buchholtz (2014) currently, the ethics linked with sustainable extraction has been established around 2 key concepts corporate social responsibility (CSR) and transparency. Transparency initiatives focus on exposure of revenue transactions between the public and private sectors within the extractive industry projects. On the other hand, corporate responsibility focuses on enhancing the association between the communities and companies (Carroll & Buchholtz, 2014). The goal of making transparency efforts has triggered legislative activity and advocacy in the UK, U.S. and Canada which are the host markets for majority of the global mining shares. Companies are employing greater resources and staff for ensuring the benefits of mining development reaching the communities as improved education, infrastructure and services (Hsieh, 2006).
Artificial intelligence is growing up fast, as are robots whose facial expressions can elicit empathy and make your mirror neurons quiver. (Diane Ackerman) The moral dilemma of having robots with A.I. has been a topic on many people 's minds, and corresponding to An Article Posted On “The Atlantic”, They Concluded, “ machines are likely to take over 47 percent of today’s jobs within a few decades.” (par2) And other people disagree with A.I. because they are programmed to have feelings and that’s not genuine. according to the article “ Pros and Cons of A.I.” One of the main cons is explained as follows “A large concern of artificial intelligence is their lack of compassion and sympathy. If these robots are introduced into fields such as healthcare, how can we ensure the patients and customers comfort?” ( par4) However having A.I. might actually be a good thing when it comes to the workforce, as stated in the Article by “Pros Of Artificial Intelligence”, “Having A.I. will be more cost efficient for businesses and robots can do many things that humans can 't”. And in the Article by the New Yorker “The Lowe’s Robot and the Future of Service Work” “robots can theoretically also help to create jobs. If cars made with robots can be sold more cheaply,
...fman R. A. - "Data Mining and Knowledge Discovery" - A Review of issues and Multi- strategy Approach". Reports of the Machine Learning and Inference Laboratory, MCI 97-2, George Mason University, Fairfax, V.A. 1997. http://www.mli.gmu.edu/~kaufman/97-1.ps
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.
The data mining process will use the mapping function which involved the decision tree and also the neural network to develop. It needs the web server and the database server to be constructed in an operating database to record the browsing route of the users. The data mining will use to identify the user’s information and classify them into different classes using decision tree.