Data Mining

Data Mining and Knowledge Discovery in Databases: a Review of the Main Topics, the Law and other Social Sciences

The term knowledge discovery in databases or KDD, for short, was coined in 1989 to refer to the broad process of finding knowledge in data, and to emphasize the “high-level” application of particular Data Mining (DM) methods (Fayyad, Piatetski-Shapiro, & Smyth, 1996). Fayyad considers DM as one of the phases of the KDD process. The DM phase concerns, mainly, the means by which the patterns are extracted and enumerated from data. Nowadays, the two terms are, usually, indistinctly used. Efforts are being developed in order to create standards and rules in the field of DM with great relevance being given to the subject of inductive databases (De Raedt, 2003) (Imielinski & Mannila, 1996). Within the context of inductive databases a great relevance is given to the so called DM languages.[1]

Data Mining and the Kdd Process, the Law and other Social Sciences

Nowadays, there exists an increasing number of applications where analysis and discovery of new patterns have fuelled the research and development of new methods, all related to Machine Learning, Knowledge Extraction, Knowledge Discovery in Databases or KDD, and Data Mining. The development of Data Mining and other related disciplines has benefited from the existence of large volumes of data proceeding from the most diverse sources and domains. KDD process and methods of Data Mining allows for the discovery of knowledge in data that is hidden to humans, presenting this knowledge under different ways. In this chapter, an overview of the KDD process with special focus in the phase of Data Mining is given. A discussion on Data Mining tasks and methods, a possible classification of them, the relation of Data Mining to other disciplines, and an overview of future challenges in the field are also given.[1]

Data Mining to Identify Project Management Strategies in Learning Environments, the Law and other Social Sciences

Projects have become a key strategic working form. It is agreed that project performance must achieve its objective and be aligned with criteria that the project stakeholders establish. The usual metrics that are considered are cost, schedule and quality. Configuration for the management of projects is a matter of decision that influences the project's evolution. There also are factors like virtual teamwork and team building processes that are relevant to that evolution. Effectiveness in managing projects depends on these factors and is investigated in this work by means of Educational Data Mining as they can help to build more effective learning and operating procedures. The conclusions from this study can help higher education course designers as well as teachers and students, by making apparent the influence of smarter strategies in the learning process. In fact, the same benefits will help practitioners too, as they can improve their continuous learning procedures and adjust their project management policies and strategies.[1]

Resources

Notes and References

1. Ana González-Marcos, Joaquín Ordieres-Meré, Fernando Alba-Elías, “Data Mining to Identify Project Management Strategies in Learning Environments” (Encyclopedia of Information Science and Technology, 4th Edition, Information Resources Management Association, 2018)

Resources

Notes and References

1. Ana Funes, Aristides Dasso, “Data Mining and the KDD Process” (Encyclopedia of Information Science and Technology, 4th Edition, Information Resources Management Association, 2018)

Resources

Notes and References

1. Ana Azevedo, “Data Mining and Knowledge Discovery in Databases: a Review of the Main Topics” (Encyclopedia of Information Science and Technology, 4th Edition, Information Resources Management Association, 2018)