Methods and Techniques of Data Mining

Methods and Techniques of Data Mining

Ana Funes (Universidad Nacional de San Luis, Argentina) and Aristides Dasso (Universidad Nacional de San Luis, Argentina)
Copyright: © 2021 |Pages: 19
DOI: 10.4018/978-1-7998-3479-3.ch051


Nowadays, there is an increasing number of applications where artificial intelligence has fuelled the research and development of new methods, techniques, and tools related to knowledge acquisition 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, the relation of data mining with other disciplines is analyzed, an overview of data mining tasks and methods is presented, and also a possible classification of them is given. Finally, a brief discussion on issues associated to the discipline and future research directions are also given.
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Theoretical discussions about the possibility of creating automatons or supposedly intelligent machines have been coming for a long time. In Ernst & Newell (1969) a research was conducted on the development of a computer program with general problem-solving capabilities. The program could solve problems like the Tower of Hanoi or prove theorems in first-order predicate calculus.

Weizenbaum, J. (1976), author of a then well-known computer program initially called ELIZA and later DOCTOR that allowed parodying the role of a Rogerian psychotherapist, was surprised, he says, for three events: (i) A number of psychiatrists thought that the program could become an automatic form of psychotherapy. (ii) How quickly and deeply people became emotionally involved with the program. (iii) The dissemination of the belief that the program showed a general solution to the problem of understanding natural language by computers. This situation reported by Weizenbaum can be compared with The Turk, the well-known chess player who for decades, from the end of the 18th century until the end of the 19th century, was believed to be a machine capable of playing chess. This, and many others around the same time, can be predecessors of programs that nowadays are considered to be part of the general field of AI.

In his textbook Wiston (1977) says that “The central goals of AI are to make computers more useful and to understand the principles which make intelligence possible”.

Many authors agree that AI is an important knowledge area that is closely related to other disciplines. As Russell, S. J. & Norvig, P. (2010) express, “Artificial Intelligence (AI) is a big field ... which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers”. AI comprises not only Machine Learning but other disciplines such as Robotics, Logic Programming and Inductive Logic Programming (ILP), a field of Machine Learning and Data Mining. These different areas could conceivably be either form part of AI or have sprout from it.

Key Terms in this Chapter

Automated Reasoning: To use stored information to answer questions and to draw conclusions.

Robotics: The machine should be able to manipulate objects and move about.

Natural Language Processing: The software is to be able to communicate successfully in a Natural Language (e.g., English).

Machine Learning: The study and use of algorithms and mathematical and statistical models in computer systems to perform a specific tasks but not using explicit instructions, on the contrary relying on discovery of patterns and inference. Is considered part of Artificial Intelligence. The software is to adapt to new circumstances and to detect and extrapolate patterns.

Clustering: Inductive task where a set of unlabeled objects is partitioned into groups (clusters) and where objects in a same cluster have similar characteristics, maximizing the similarity intra cluster and minimizing the similarity inter cluster.

Classification: Inductive task where a predictive model is learnt from objects labeled with a class and whereby it is possible to predict the class of new objects.

Inductive Learning: Induction is the inference of information from data and inductive learning is a model building process where the data are analyzed to find hidden patterns.

Cloud Computing: Generally refers to describe data centers available to many users over the Internet that provide data storage and computing power. It’s sometimes used to process DNNs.

Weak AI: Also kwon as Narrow AI is AI aimed as a particular task.

AI4Good: By building lasting communities that bring the best technologies to bear on the world’s most important challenges, the AI for Good Foundation drives forward solutions that support the United Nations Sustainable Development Goals (SDG’s). We do this by coordinating the AI research community, technologists, data, and infrastructure with the stakeholders on the ground, policy makers, and the broader public ( ).

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