Data Mining Methods and Applications (Discrete Mathematics and Its Applications)

Data Mining Tutorial
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Students will learn to translate business-related problems into simple equations.

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Topics include applications of ratio and proportion, computing taxes, commercial discounts, simple and compound interest, basic statistics, and graphs. This course will emphasize the use of basic algebra concepts in solving numerical problems common in business and management. Students will apply skills of writing, solving, and graphing elementary equations. Students will apply basic linear programming methods to management science problems.

Handbook of Statistical Analysis and Data Mining Applications | ScienceDirect

They expect better metadata offering rich data about the documents and are willing to participate in the metadata creation and sharing process. Students will apply basic linear programming methods to management science problems. In the first step, a classification model based on previous data is build. Enter pincode. Thus, in this approach, the "network" is the functional equivalent of a model of relations between variables in the traditional model building approach. After a large set of rules are generated, AC selects a subset of high-quality rules via rule pruning and ranking. Encryption is another technique in which individual data items may be encoded.

Covers various topics of mathematics that are both conceptual and practical. Course is designed to enable a student to appreciate mathematics and its application to numerous disciplines and professions. This course explores algebra through the lens of the modular systems, each a finite and unique world generated by remainders.

Students will develop number sense, problem-solving skills, and a deeper understanding of arithmetic and algebra as they experience the beauty, underlying structure, surprising results, and creative potential of mathematics. Covers sets, the real number system, functions, equations, inequalities, and logarithms.

Presents trigonometric functions using the unit circle. Prerequisite : MATH or equivalent competence.

Introduction to Discrete Mathematics for Computer Science Specialization

Introduces the ideas of calculus without the rigor associated with the course in the standard calculus sequence. It can be used by students who are not mathematics or science majors to understand the concepts of calculus well enough to apply them to their own discipline. It might also be used as a stepping stone to get a head start before taking the standard calculus course. The emphasis is on computational ability, problem solving, and applications.

Prerequisite : Proficiency in algebra. Studies set terminology and operations, subsets, the power set, Cartesian products, and finite cardinality, relations as sets of ordered pairs, characteristic functions, digraphs, functions as relations, types of functions and relations. Prerequisite : MATH Covers all the fundamental topics in deductive logic.

A thorough introduction to propositional and predicate logic. Introduces differential and integral calculus of one variable, culminating in the fundamental theorem of calculus. Introduces calculus of transcendental functions. Only offered in a week format. May be repeated once for credit. Continues the study of calculus: the transcendental functions, techniques of integration, applications of the integral, polar coordinates, parametric equations, sequences, and series. This course serves as a transition course from calculus to abstract mathematics. The goal is to make it sufficiently difficult for adversaries to use combinations of record attributes to exactly identify individual records.

Distributed privacy preservation : Large data sets could be partitioned and distributed either horizontally i. While the individual sites may not want to share their entire data sets, they may consent to limited information sharing with the use of a variety of protocols.

Course Descriptions

The overall effect of such methods is to maintain privacy for each individual object, while deriving aggregate results over all of the data. Downgrading the effectiveness of data mining results : In many cases, even though the data may not be available, the output of data mining e.

Data Mining

The solution could be to downgrade the effectiveness of data mining by either modifying data or mining results, such as hiding some association rules or slightly distorting some classification models. Recently, researchers proposed new ideas in privacy-preserving data mining such as the notion of differential privacy. The general idea is that, for any two data sets that are close to one another i. This definition gives a strong guarantee that the presence or absence of a tiny data set e.

Shortest network problems

Data Mining Methods and Applications (Discrete Mathematics and Its Applications) [Kenneth D. Lawrence, Stephan Kudyba, Ronald K. Klimberg] on. Data Mining Methods and Applications (Discrete Mathematics and Its Applications) eBook: Kenneth D. Lawrence, Stephan Kudyba, Ronald K. Klimberg.

Based on this notion, a set of differential privacy-preserving data mining algorithms have been developed. Research in this direction is ongoing. We expect more powerful privacy-preserving data publishing and data mining algorithms in the near future. Like any other technology, data mining can be misused.

However, we must not lose sight of all the benefits that data mining research can bring, ranging from insights gained from medical and scientific applications to increased customer satisfaction by helping companies better suit their clients' needs. We expect that computer scientists, policy experts, and counterterrorism experts will continue to work with social scientists, lawyers, companies, and consumers to take responsibility in building solutions to ensure data privacy protection and security.

In this way, we may continue to reap the benefits of data mining in terms of time and money savings and the discovery of new knowledge. Rick F. A business intelligence system is a solution developed to support and improve the decision-making process of an organization. A wide range of tools is available that is designed specifically to support this decision-making process. All those different tools can be classified in two main categories: reporting tools and analytical tools. Reporting tools allow users to study, filter, aggregate, summarize data and so on. In most cases, what is presented to the users is what has happened in the organization.

Analytical tools are based on statistics, data mining, and operations research, and they support algorithms for forecasting, predictive analysis and optimization. To summarize, reporting tools show what has happened looking backward , whereas analytical tools show what will possibly happen and how processes can be improved looking forward. Both categories of tools consist of multiple subcategories. Table 2. More and more, products offer sufficient functionality to belong to multiple categories.

What most users see of a business intelligence system is the user interface of their reporting or analytical tool, which is the way it should be. Users know that the data they need for their decision making is gathered in production systems, such as the invoice management system, the sales system, and the finance system. But how that data gets to the reports is not relevant for them. As long as they have access to the right data, as long as it is accurate and trustworthy , as long as it has the right quality level, and as long as the performance of the tool is fast enough, they are satisfied.

This is comparable to cell phones. Figure 2. To users, a business intelligence system is, and should be, a black box. But to create those reports, a complex architecture has to be designed and developed to get the right data in the right form and at the right time from the production systems to the reports. Such an architecture consists of many components, tools, and data stores. All these components are described in the following sections. There are two types of infrastructure required for the social sciences to flourish. While the two are related on an empirical level, they are kept analytically distinct for this article.

The second type of infrastructure is required to conduct research itself. What this image misses are less tangible research resources, such as databases and data mining tools that sustain and advance the research enterprise. Research infrastructure emphasizes shared and sustaining resources that not only make scientific research possible, but also to add value to research activity.

For example, a multimillion-dollar investment in building an instrument for magnetic imaging of the mind is much like a similar investment in a year panel study of a nation's youth as they leave school and enter the workforce. For example, multiple time series of different topical domains might cross-reference brain images over time in cognitive and in emotional activity, or, from the same sampled cohort, might interrelate the economic outcomes of the school to work transition and of the trajectories of social development over the same period.

For example, with respect to scholarly infrastructure, if the funding of the National Science Foundation NSF were cut in half for a 4-year period because of a change in priorities, research universities might be forced to close some institutes, and some might even redirect their missions. From a research infrastructure standpoint, a year time series on the American or Canadian electorate, broken for a decade, may never retrieve the value it would have had were it not interrupted. HathiTrust, named in , includes both digitized books and journal articles. This digital library contains materials in both the public domain and copyrighted works.

The main issues are quality control, public search interfaces, ingestion of non-Google and nonbook content, access issues for people with disabilities, collection grouping, data mining, and academic research tools. HathiTrust is not dependent on the Google Book Project, and it has more resources from the public domain.

As of August , the HathiTrust had more than partners, and it is open to institutions all over the world. It contained more than 6 million book titles and , serial titles HathiTrust, n. The main objective of HathiTrust is to create a comprehensive digital collection of library materials owned by the participating research institutions. It is not only a digital library but also a collaborative group that works on key issues in creating and preserving a large collection of digital volumes.

The main challenge facing HathiTrust is copyright. Researchers can search for copyrighted documents but are unable to access them if their institutions are not members. User feedback is a key for the creation of a successful digital library.

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The analysis from focus groups and interviews indicates that scholars consider collection building as a key scholarly activity and highly heterogeneous. They expect better metadata offering rich data about the documents and are willing to participate in the metadata creation and sharing process.

Rough Set Theory - Indiscernibility - Set Approximation - Solved Example

After comparing the functionality of Google Books and HathiTrust on federal government publication use, Sare concludes that Google Books and HathiTrust each has its own strength and limitation. While Google Books has more government documents in general, HathiTrust is best for locating full-text government documents published after Google Books has an advantage in providing the added functionality of data visualization. Higher-education analysts have not generally been in the vanguard of implementing predictive methods in their work.

Yet, predicting various student outcomes including retention, graduation, placement, and licensure exam passage rates can provide college administrators with valuable information about their students and graduates and may help devise ways to assist those at risk before it is too late. This case study is an illustration of how one can approach such a problem and what can be done in a reasonably short period of time.

While working on this project, we have developed several principles higher-education researchers and analysts in other industries can benefit from. First, it is definitely worth searching for strong predictors that make sense from your theory's standpoint before moving on to utilizing complex data mining techniques in hopes of making your many weak predictors work better. In many ways, consumer goods companies that have been at the forefront of applied data mining research have had a disproportionately large influence on the way data mining procedures developed.

These companies operate in a world lacking credible information: Quite often, their researchers work with data self-reported by consumers or potential buyers, and the quality of such data can never be fully insured.