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

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Practical Machine Learning Tools and Techniques

A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research.

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A Survey on Transfer Learning

Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.

A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise

Knowledge discovery in databases (or data mining), which extracts interesting knowledge from large databases, represents an important direction in the development of data-and knowledge-base systems. With fruitful research results on knowledge discovery in re-lational databases and the emerging trend in the development of object-oriented and active database systems , it is natural to investigate knowledge discovery in object-oriented and active databases. This paper overviews the mechanisms for knowledge discovery in object-oriented and active database systems, with an emphasis on the techniques for generalization of complex data objects, methods, class hierarchies and dynamically evolving data, and on the integration of knowledge discovery mechanisms with production control processes.

Knowledge Discovery in Object-Oriented and Active Databases

Owing to the rapid mounting of massive image data, image classification has attracted lots of research efforts. Several diverse research disciplines have been confluent on this important theme, looking for more powerful solutions. In this paper, we propose a novel image representation method B2S (Bag to Set) that keeps all frequency information and is more discriminative than traditional histogram based bag representation. Based on B2S, we construct two different image classification approaches. First, we apply B2S to a state-of-the-art image classification algorithm SPM in computer vision. Second, we design a framework DisIClass (Discriminative Frequent Pattern-Based Image Classification) to utilize data mining algorithms to classify images, which was hardly done before due to the intrinsic differences between the data of computer vision and data mining fields. DisIClass adapts the locality property of image data, and apply sequential covering method to induce the most discriminative feature sets from a closed frequent item set mining method. Our experiments with real image data show the high accuracy and good scalability of both approaches.

DisIClass: discriminative frequent pattern-based image classification

Nowadays, most business, administration, and/or scientific databases contain both structured attributes and text attributes We call a database that consists of both multidimensional structured data and narrative text data as multidimensional text database Searching, OLAP, and mining such databases pose many research challenges To enhance the power of data analysis, interesting entities and relationships can be extracted from such databases to derive heterogeneous information networks, which in turn will substantially increase the power and flexibility of data exploration in such databases Based on our previous studies on TextCube [1], TopicCube [2], and information network analysis, such as RankClus [3] and NetClus [4], we construct iNextCube, an information-Network-enhanced textCube In this demo, we show the power of iNextCube in the search and analysis of two multidimensional text databases: (i) a DBLP-based CS bibliographic database, and (ii) an online news database

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iNextCube: information network-enhanced text cube

Discovery of Frequent Substructures

Online bibliographic databases, such as DBLP in computer science and PubMed in medical sciences, contain abundant information about research publications in different fields. Each such database forms a gigantic information network (hence called BibNet), connecting in complex ways research papers, authors, conferences/journals, and possibly citation information as well, and provides a fertile land for information network analysis. Our BibNetMiner is designed for sophisticated information network mining on such bibliographic databases. In this demo, we will take the DBLP database as an example, demonstrate several attractive functions of BibNetMiner, including clustering, ranking and profiling of conferences and authors based on the research subfields. A user-friendly, visualization-enhanced interface will be provided to facilitate interactive exploration of a bibliographic database. This project will serve as an example to demonstrate the power of links in information network mining. Since the dataset is large and the network is heterogeneous, such a study will benefit the research on the analysis of massive heterogeneous information networks.

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Jiawei Han

Papers

Knowledge Discovery in Object-Oriented and Active Databases

DisIClass: discriminative frequent pattern-based image classification

iNextCube: information network-enhanced text cube

Discovery of Frequent Substructures

BibNetMiner: mining bibliographic information networks