information visualization in data mining and knowledge discovery pdf Saturday, May 8, 2021 12:07:22 PM

Information Visualization In Data Mining And Knowledge Discovery Pdf

File Name: information visualization in data mining and knowledge discovery .zip
Size: 1716Kb
Published: 08.05.2021

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Book Published Computer Science. From the Publisher: Mainstream data mining techniques significantly limit the role of human reasoning and insight.

An Overview of Big Data Visualization Techniques in Data Mining

Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning , statistics , and database systems. The term "data mining" is a misnomer , because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction mining of data itself. The book Data mining: Practical machine learning tools and techniques with Java [8] which covers mostly machine learning material was originally to be named just Practical machine learning , and the term data mining was only added for marketing reasons. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records cluster analysis , unusual records anomaly detection , and dependencies association rule mining , sequential pattern mining. This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system.

Mainstream data mining techniques significantly limit the role of human reasoning and insight. Likewise, in data visualization, the rote of computational analysis is relatively small. The power demonstrated individually by these approaches to knowledge discovery suggests that somehow uniting the two could lead to increased efficiency and more valuable results. But is this true? How might it be achieved?

Applying visual representation in the KDD process aims to facilitate the understanding over its results. Thus, visualization techniques can be integrated into the process of KDD in three different ways: to preview the data to be analyzed; to help in understanding the results of data mining, or to understand the partial results of the iterations inherent in the process of extracting knowledge [ 2 ]. However, the exploration and analysis of data using visualization techniques can bring new and enough knowledge exempting the use of other data mining techniques. Furthermore, the visualization is a powerful tool for conveying ideas, due to the vision plays an important role in human cognition [ 7 ]. In the visualization process, it is relevant to consider the choice of the best technique to be used in a certain application or situation.

Synthesis Lectures on Data Mining and Knowledge Discovery

The central idea dealt in this work relies on how to perform data mining tasks in a visual fashion; that is, using graphical correlation and interaction techniques. The scope of this review encompasses visualization techniques, formal visualization systems, and smart information visualization models. As well, newest approaches consisting of visualization and data mining integration process are explained. Introduction Juan C. Typically, such systems extract meaningful knowledge from Big Data through rules and models. In this sense, data mining DM has shown to be a collection of powerful techniques. Nonetheless, when dealing with Big Data the resultant set of rules is usually represented in a plain fashion, requiring then data analyst to have special skills and expertise to understand such rules and patterns to make the extracted knowledge more intelligible.

Knowledge discovery in databases from a perspective of intelligent information visualization

Mainstream data mining techniques significantly limit the role of human reasoning and insight. Likewise, in data visualization, the rote of computational analysis is relatively small. The power demonstrated individually by these approaches to knowledge discovery suggests that somehow uniting the two could lead to increased efficiency and more valuable results. But is this true?

To browse Academia. Skip to main content. By using our site, you agree to our collection of information through the use of cookies. To learn more, view our Privacy Policy.

Data mining

Information Visualization in Data Mining and Knowledge Discovery

Mainstream data mining techniques significantly limit the role of human reasoning and insight. Likewise, in data visualization, the role of computational analysis is relatively small. The power demonstrated individually by these approaches toMoreMainstream data mining techniques significantly limit the role of human reasoning and insight. The power demonstrated individually by these approaches to knowledge discovery suggests that somehow uniting the two could lead to increased efficiency and more valuable results. But is this true? How might it be achieved? And what are the consequences for data-dependent enterprises?

Knowledge discovery in databases from a perspective of intelligent information visualization

Tente novamente mais tarde. Adicionar coautores Coautores. Carregar PDF. PDF Restaurar Eliminar definitivamente. Seguir este autor.

The series publishes to page publications on topics pertaining to data mining, web mining, text mining, and knowledge discovery, including tutorials and case studies. Sign in to personalize your visit. New user?

0 Comments

LEAVE A COMMENT