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 VAT method is a visual technique for determining the potential cluster structure and the possible number of clusters in numerical data. Its improved version, iVAT, uses a path-based distance transform to improve the effectiveness of VAT for "tough" cases. Both VAT and iVAT have also been used in conjunction with a single-linkage(SL) hierarchical clustering algorithm. However, they are sensitive to noise and bridge points between clusters in the dataset, and consequently, the corresponding VAT/iVAT images are often in-conclusive for such cases. In this paper, we propose a constraint-based version of iVAT, which we call ConiVAT, that makes use of background knowledge in the form of constraints, to improve VAT/iVAT for challenging and complex datasets. ConiVAT uses the input constraints to learn the underlying similarity metric and builds a minimum transitive dissimilarity matrix, before applying VAT to it. We demonstrate ConiVAT approach to visual assessment and single linkage clustering on nine datasets to show that, it improves the quality of iVAT images for complex datasets, and it also overcomes the limitation of SL clustering with VAT/iVAT due to "noisy" bridges between clusters. Extensive experiment results on nine datasets suggest that ConiVAT outperforms the other three semi-supervised clustering algorithms in terms of improved clustering accuracy.

ConiVAT: Cluster Tendency Assessment and Clustering with Partial Background Knowledge.

Since the beginning of the domestic oil industry in 1859, the oil exploration process has been dependent on rules-of-thumb and best-guess strategies of expert geologists In the absence of well-defined methods for collecting, analyzing, and interpreting geological data, oil exploration involved much guesswork and extreme financial risks, especially in the beginning when it was conducted "on a basis of feast or famine" The nature of oil exploration is such that it takes many years (sometimes decades) for a geologist to gain the knowledge and experience needed for analyzing geological data in a reliable and efficient manner Exposure to a wide range of situations seems to be the key to developing expertise in the field Another important issue that impacts oil exploration is the acquisition and handling of the diverse and vast amounts of data that are needed by experts to make reliable conclusions

MAGIK : A System For Hydrocarbon Play Analysis

The method involves selecting a membership function type from a predetermined number of membership function types. Initialising parameter are allocated to several membership functions. For each membership function and for all training data vectors are iterative steps carried out until an abort criterion is fulfilled. Membership values for training data vectors determined and stored using clusters describing membership functions. A centre value of the membership function is determined and stored using the membership values. A further iteration is carried out in which the membership function in each iteration is described by the centre value determined in the previous iteration. The determined membership functions are describes by the centre values of the last iteration and the determined membership functions are used as rules.

Method and apparatus for the design of fuzzy logic rules from training data vectors

I discuss four technologies : fuzzy pattern recognition (numerical and syntactic), computational neural networks, and fuzzy control. I'm not sure what a critical review is, so let me warn you that this is not a survey of recent or important work in any of these fields. Infant technologies usually develop a lot of internal structure before they reach out towards applications in companion fields. The purpose of this article is to assess the maturation of these disciplines by giving two examples of cross fertilization between control and pattern recognition. First, I illustrate the use of pattern recognition - fuzzy clustering and feed forward neural networks - to help develop and represent fuzzy controllers. And second, I give an example of wave form analysis by syntactic pattern recognition that uses fuzzy control logic. I concludes with some ideas about what should happen next - and what may happen next - in terms of hybridization between the four disciplines.

James C. Bezdek

Papers

ConiVAT: Cluster Tendency Assessment and Clustering with Partial Background Knowledge.

MAGIK : A System For Hydrocarbon Play Analysis

Method and apparatus for the design of fuzzy logic rules from training data vectors

Pattern recognition, neural networks, fuzzy control: what's next?

PASI: Interpolation and Approximation in Fuzzy Rule Bases