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

We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework for topical keyphrase generation and ranking. By shifting from the unigram-centric traditional methods of unsupervised keyphrase extraction to a phrase-centric approach, we are able to directly compare and rank phrases of different lengths. We construct a topical keyphrase ranking function which implements the four criteria that represent high quality topical keyphrases (coverage, purity, phraseness, and completeness). The effectiveness of our approach is demonstrated on two collections of content-representative titles in the domains of Computer Science and Physics.

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KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles.

Photo-sharing services have attracted millions of people and helped construct massive social networks on the Web. A popular trend is that users share their image collections within social groups, which greatly promotes the interactions between users and expands their social networks. Existing systems have difficulties in generating satisfactory social group suggestions because the images are classified independently and their relationship in a collection is ignored. In this work, we intend to produce suggestions of suitable photo-sharing groups from a user's personal photo collection by mining images on the Web and leveraging the collection context. Both visual content and textual annotations are integrated to generate initial prediction of the events or topics depicted in the images. A user collection-based label propagation method is proposed to improve the group suggestion by modeling the relationship of images in the same collection as a sparse weighted graph. Experiments on real user images and comparisons with the state-of-the-art techniques demonstrate the effectiveness of the proposed approaches.

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Social group suggestion from user image collections

Air that is desired to be examined for impurities is drawn through a concentrator comprising a cylindrical receptacle into which the air is introduced at the base portion of the receptacle in a direction tangentially of the cylindrical wall. A scrubbing liquid such as water is supplied to the receptacle and withdrawn therefrom on a continuous basis. The quantity of water only partially fills the region at the base of the receptacle where the air is tangentially introduced, with the result that the water is caused to rotate vigorously within the cylindrical wall as a thin film. The air is initially beneath the rotating film but then passes upwardly through it and is efficiently scrubbed. Gaseous and particulate impurities in the air are thus extracted into the water, and the same is drained from the receptacle and monitored for contaminant content.

Feature-based Substructure Similarity Search

Plans or sequences of actions are an important form of data With the proliferation of database technology plan databases or planbases are increasingly common E cient discovery of important patterns of actions in plan databases presents a challenge to data mining In this paper we present a method for mining signi cant patterns of successful actions in a large planbase using a divide and conquer strategy The method exploits multi dimensional generalization of sequences of actions and extracts the inherent hierarchical structure and sequential patterns of plans at di erent levels of abstraction These patterns are used in turn to subsequently narrow down the search for more speci c patterns The process is analogous to the use of divide and conquer methods in hierarchical planning We illustrate our approach using a travel planning database

http://www.cs.ust.hk/~qyang/Docs/1999/han.pdf

Plan Mining by Divide-and-Conquer

We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. While prior methods treat the problem as a two-phase task (i.e., detecting hypernymy pairs followed by organizing these pairs into a tree-structured hierarchy), we argue that such two-phase methods may suffer from error propagation, and cannot effectively optimize metrics that capture the holistic structure of a taxonomy. In our approach, the representations of term pairs are learned using multiple sources of information and used to determine \textit{which} term to select and \textit{where} to place it on the taxonomy via a policy network. All components are trained in an end-to-end manner with cumulative rewards, measured by a holistic tree metric over the training taxonomies. Experiments on two public datasets of different domains show that our approach outperforms prior state-of-the-art taxonomy induction methods up to 19.6\% on ancestor F1.

Jiawei Han

Papers

KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles.

Social group suggestion from user image collections

Feature-based Substructure Similarity Search

Plan Mining by Divide-and-Conquer

End-to-End Reinforcement Learning for Automatic Taxonomy Induction