There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

/pdf/data-mining-concepts-and-techniques-4dtvdfkvmi.pdf

Data Mining: Concepts and Techniques

New updated! The latest book from a very famous author finally comes out. Book of pattern recognition with fuzzy objective function algorithms, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.

Pattern Recognition with Fuzzy Objective Function Algorithms

Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

/pdf/data-clustering-a-review-4c452f5cab.pdf

Data clustering: a review

Data Mining - Concepts and Techniques.

One of the applications that motivates this research is a system for detection of the anomalies in wireless sensor networks (WSNs). Individual sensor measurements are converted to ellipsoidal summaries; a data matrix D is built using a dissimilarity measure between pairs of ellipsoids; clusters of ellipsoids are suggested by dark blocks along the diagonal of an iVAT (improved Visual Assessment of Tendency) image of D; and finally, the single linkage algorithm extracts clusters from D, using the iVAT image as a guide to the selection of an optimal partition. We illustrate this model for higher dimensional data with synthetic, real and benchmark data sets. Our examples show that two of the four measures, viz, Focal distance and Bhattacharyya distance, provide very similar and reliable bases for estimating cluster structures in sets of higher dimensional ellipsoids, that single linkage can successfully extract the indicated clusters, and that our model can find both first and second order anomalies in WSN data.

Measures for clustering and anomaly detection in sets of higher dimensional ellipsoids

Applications and Science of Computational Intelligence

In this note we discuss an extension of the fuzzy ISODATA (or fuzzy k-means) clustering algorithms, which attempt to recognize and compensate for their failure at detection of linear substructure in finite data sets. A numerical example is given to substantiate the proposed extension: and we offer some theoretical conjectures concerning its construction.

On the extension of fuzzy k-means algorithms for detection of linear clusters

Linguistic summarization of time series can glean meaningful information from huge amounts of data. However, in situations like continuous monitoring, even linguistic summaries become difficult for a person to understand. In this paper, we develop an approach to generate linguistic prototypes from a group of time blocks that represent a normal condition. Then the set of summaries for new time blocks are compared to the prototypes to flag anomalous conditions, thereby reducing the burden on the human. Case studies from an eldercare environment demonstrate the utility of this approach.

Generation of prototypes from sets of linguistic summaries

We propose a method for training a standard feed forward, back propagation neural-like network using fuzzy label vectors whose performance goal is to produce edge images from standard imagery such as FLIR, video, and grey tone pictures. Our method is based on training the network on a basis set of edge windows which are scored using the Sobel operator. The method is illustrated by comparing edge images of several real scenes with those derived using the Sobel and Canny image operators.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

James C. Bezdek

Papers

Measures for clustering and anomaly detection in sets of higher dimensional ellipsoids

Applications and Science of Computational Intelligence

On the extension of fuzzy k-means algorithms for detection of linear clusters

Generation of prototypes from sets of linguistic summaries

Edge detection using a fuzzy neural network