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

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

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Community detection in graphs

11 In this paper a novel clustering algorithm is proposed as a version of the 12 Seeded Region Growing (SRG) approach for the automatic recognition of 13 coastal upwelling from Sea Surface Temperature (SST) images. 14 The new algorithm, One Seed Expanding Cluster (SEC), takes advan15 tage of the concept of approximate clustering due to Mirkin (1996, 2013) to 16 derive a homogeneity criterion in the format of a product rather than the 17 conventional difference between a pixel value and the mean of values over 18 the region of interest. It involves a boundary-oriented pixel labeling so that 19 the cluster growing is performed by expanding its boundary iteratively. The 20 starting point is a cluster consisting of just one seed, the pixel with the cold21 est temperature. The baseline version of the SEC algorithm uses the Otsu’s 22 thresholding method to fine-tune the homogeneity threshold. Unfortunately, 23 this method does not always lead to a satisfactory solution. Therefore, we 24 introduce a self-tuning version of the algorithm in which the homogeneity 25 threshold is locally derived from the approximation criterion over a window 26 around the pixel under consideration. The window serves as a boundary 27 regularizer. 28 Department of Computer Science and NOVA Laboratory for Computer Science and Informatics (NOVA-LINCS), Faculdade de Ciencias e Tecnologia, Universidade Nova de Lisboa 2829-516 Caparica, Portugal. tel.:+351-21-294 8536; fax: +351-21-294 8541. Email address: snt@fct.unl.pt (Susana Nascimento) Preprint submitted to Elsevi Mar h 4, 2015 These two unsupervised versions of the algorithm have been applied to a 29 set of 28 SST images of the western coast of mainland Portugal, and com30 pared against a supervised version fine-tuned by maximizing the F -measure 31 with respect to manually labeled ground-truth maps. The areas built by the 32 unsupervised versions of the SEC algorithm are significantly coincident over 33 the ground-truth regions in the cases at which the upwelling areas consist of 34 a single continuous fragment of the SST map. 35

A Seed Expanding Cluster Algorithm for Deriving

Research effort has recently focused on designing feature weighting clustering algorithms. These algorithms automatically calculate the weight of each feature, representing their degree of relevance, in a data set. However, since most of these evaluate one feature at a time they may have difficulties to cluster data sets containing features with similar information. If a group of features contain the same relevant information, these clustering algorithms set high weights to each feature in this group, instead of removing some because of their redundant nature. This paper introduces an unsupervised feature selection method that can be used in the data pre-processing step to reduce the number of redundant features in a data set. This method clusters similar features together and then selects a subset of representative features for each cluster. This selection is based on the maximum information compression index between each feature and its respective cluster centroid. We present an empirical validation for our method by comparing it with a popular unsupervised feature selection on three EEG data sets. We find that our method selects features that produce better cluster recovery, without the need for an extra user-defined parameter.

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A Clustering-Based Approach to Reduce Feature Redundancy

Detecting Communities in Feature-Rich Networks with a K-Means Method

After a short introduction of the general concept of decision rule to relate input and target features, this chapter describes most popular methods for decision rule building. Two of them pertain to quantitative targets (linear regression, neural network), and four to categorical ones (linear discrimination, support vector machine, naive Bayes classifier and classification tree). Of these, classification trees are treated in a most detailed way including a number of theoretical results that are not well known. These establish firm relations between popular scoring functions and, first, bivariate measures described in Chapter 3– Quetelet indexes in contingency tables, first of all – and, second, normalization options for dummy variables representing target categories. Some related concepts such as Bayes decision rule, bag-of-word model in text analysis, VC-complexity and kernel for non-linear classification are introduced too.

Learning Multivariate Correlations in Data

Empirical research effort over Corporate Social Responsibility (CSR) is typically concentrated on a limited number of aspects. We focus on the whole set of CSR activities to find out if there is a structure in those. We take data on the four major dimensions of CSR: environment, social & stakeholder, labor, and governance, from the MSCI database. To find out the structure hidden under almost constant average values, we apply a modification of K-means clustering with its complementary criterion. This method leads us to discover an impressive process of change in patterns that we predict will continue in the future.

Boris Mirkin

Papers

A Seed Expanding Cluster Algorithm for Deriving

A Clustering-Based Approach to Reduce Feature Redundancy

Detecting Communities in Feature-Rich Networks with a K-Means Method

Learning Multivariate Correlations in Data

Deriving Corporate Social Responsibility Patterns in the MSCI Data