Data Mining

7/04/2016 03:14:00 PM Data Mining 0 Comments

The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytic.
 

Data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike.

Exploratory data analysis aims to explore the numeric and categorical attributes of the data individually or jointly to extract key characteristics of the data sample via statistics that give information about the centrality, dispersion, and so on. Moving away from the IID assumption among the data points, it is also important to consider the statistics that deal with the data as a graph, where the nodes denote the points and weighted edges denote the connections between points. This enables one to extract important topological attributes that give insights into the structure and models of networks and graphs. Kernel methods provide a fundamental connection between the independent point wise view of data, and the viewpoint that deals with pairwise similarities between points. Many of the exploratory data analysis and mining tasks can be cast as kernel problems via the kernel trick, that is, by showing that the operations involve only dot-products between pairs of points. However, kernel methods also enable us to perform nonlinear analysis by using familiar linear algebraic and statistical methods in high-dimensional spaces comprising “nonlinear” dimensions.

Frequent pattern mining refers to the task of extracting informative and useful patterns in massive and complex data sets. Patterns comprise sets of co-occurring attribute values, called itemsets, or more complex patterns, such as sequences, which consider explicit precedence relationships (either positional or temporal), and graphs, which consider arbitrary relationships between points. The key goal is to discover hidden trends and behaviors in the data to understand better the interactions among the points and attributes.

Clustering is the task of partitioning the points into natural groups called clusters, such that points within a group are very similar, whereas points across clusters are as dissimilar as possible. Depending on the data and desired cluster characteristics, there are different types of clustering paradigms such as representative-based, hierarchical, density-based, graph-based, and spectral clustering.

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