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Information Visualization In Data Mining And Knowledge Discovery Pdf

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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 ].

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Knowledge discovery in databases from a perspective of intelligent information visualization

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.

Information Visualization in Data Mining and Knowledge Discovery by Usama Fayyad

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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?

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. Likewise, in data visualization, the role of computational analysis is relatively small.

Synthesis Lectures on Data Mining and Knowledge Discovery

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.

1. Introduction

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? And what are the consequences for data-dependent enterprises? Information Visualization in Data Mining and Knowledge Discovery is the first book to ask and answer these thought-provoking questions.

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Information Visualization in Data Mining and Knowledge Discovery

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