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

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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.

The fuzzy c-means algorithm is a useful tool for clustering real s-dimensional data. Typically, each observation consists of numerical values for s feature such as height, length, etc. In some cases, data sets contain vectors that are missing one or more feature values. For example, a particular datum might have the form: (254.3, x, 36.2, 112.7, x), where the second and fifth feature values are missing. The (standard) fuzzy c-means algorithm cannot be applied in this case since the required computations reference numerical features values for all s features of every data point. Two adaptations of fuzzy c-means to the incomplete data case are presented here. One adaptation replaces unknown feature values with additional variables that are optimized to prove an extrapolated data set yielding the smallest possible value of the fuzzy c-means criterion. Another approach uses only the available feature values in distance calculations, and then adjusts for the missing feature values by an appropriately chosen scaling of the computed distances. Numerical convergence properties of the adaptations and computational costs are discussed. Artificial data sets are used to demonstrate the two new approaches.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Fuzzy c-means clustering of partially missing data sets

This paper discusses the design and implementation of PLAYMAKER: a knowledge base system for characterizing hydrocarbon plays. PLAYMAKER is a component of XX (eXpert eXplorer), a workstation-based tool that aids exploration geologists in a number of different tasks: sediment and carbonates simulation, play and field characterization, retrieval and storage of information in a geological database, comparison of play or field under study with other fields in the database, and report generation. PLAYMAKER is implemented using MIDST (Mixed Inferencing Dempster Shafer Tool), a rule-based expert system shell that incorporates mixed-iniative reasoning and inexact reasoning based on the Dempster-Shafer evidence combination scheme. This paper discusses the effectiveness of a two-level knowledge base structure adopted for the design and implementation of PLAYMAKER.

James C. Bezdek

Papers

Fuzzy c-means clustering of partially missing data sets

PLAYMAKER: a knowledge-based approach to characterizing hydrocarbon plays

Guest editorial and conference report

Applications of Fuzzy Logic Technology III

Fuzzy Computation in Practice