Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

/pdf/social-media-and-fake-news-in-the-2016-election-1m4ttkb5mo.pdf

Social Media and Fake News in the 2016 Election

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Extracting required patterns from huge amount of mixed data is an area of interest to the researchers. Various promising and already established algorithms are currently using in the name of Data Clustering. Clustering is used for partitioning the data into number of data sets or group. In this paper, we review four popular clustering algorithms from data mining perspective.

Process of Extracting Uncover Patterns from Data: A Review

Knowledge engineering is one of the key research area to build knowledgebase for providing solutions to real-world problems. Due to rapidly increase of data growth rate, it is almost impossible to extract hidden knowledge with manual approach. Moreover, a number of methodologies have been proposed that focus on some specific aspect of the data mining process rather than end-to-end knowledge engineering methodology. Keeping in view these facts, a Semi-automatic Knowledge Engineering Methodology (SaKEM) is proposed that covers all major stages that are involved in Knowledge Discovery in Databases (KDD) process. For realization of SaKEM, a toolset called Data Driven Knowledge Acquisition Tool (DDKAT) is developed. The proposed methodology is designed for Mining Minds project but it can be utilized by other service-enabled platforms as well.

/pdf/sakem-a-semi-automatic-knowledge-engineering-methodology-for-3y8e5uwc7j.pdf

SaKEM: A Semi-automatic Knowledge engineering methodology for building rule-based knowledgebase

A new Ripple Down Rules based methodology which allows for the creation of rules that use classifications as conditions has been developed, and is entitled Multiple Classification Ripple Round Rules (MCRRR). Since it is difficult to recruit human experts in domains which are appropriate for testing this kind of method, simulated evaluation has been employed. This paper presents a simulated evaluation approach for assessing two separate aspects of the MCRRR method, which have been identified as potential areas of weakness. Namely,"Is the method useful in practice?" and"Is the method acceptable, computationally?" It was found that the method appears to be of value in some, but not many,"traditional" multi-class domains, and that due to computational concerns with one aspect of the method it is considered unsuitable for domains with a very large number of cases or rules. These issues are discussed and solutions are proposed.

Simulated assessment of ripple round rules

Colonic polyps appear like elliptical protrusions on the inner wall of the colon. The many proposed algorithms assumed the shape of a polyp as a spherical cap, so the algorithms are not flexible when the polyps are irregular shapes. In this paper, we propose a mapping using regional unit sphere (MuRUS) method to overcome the problem caused by unexpected polyp shapes. The MuRUS has shape invariant and size invariant properties. Our method was applied to colon CT images from 37 patients each having a prone and supine scan. There are 45 colonscopically confirmed polyps. The results obtained by our algorithm were compared with those gold standards. 100% of polyps >= 10mm in diameter were detected, 90% of polyps >= 6mm in diameter were detected and 70% of polyps < 6mm in diameter were detected at 7.0 FPs per patient.

Autonomic Colonic Polyp Detection by the Mapping using Regional Unit Sphere

Notice of Retraction. Manifold learning-based fuzzy k-principal curve similarity evaluation for wind turbine condition monitoring

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