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

/pdf/data-mining-concepts-and-techniques-4dtvdfkvmi.pdf

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.

/pdf/a-survey-on-transfer-learning-1hjmu3otql.pdf

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

To discover knowledge or retrieve information from a relational database, a user often needs to find objects related to certain source objects. There are two main challenges in building an effective object search system: the huge amount of objects in the database and the large number of different relationships between objects. In this paper we introduce Ross, an efficient and accurate relational object search system. Ross accepts complex queries that enable users to specify the relationships among objects. To measure the relationships of join paths, Ross considers the different semantics of different joins, and combines both selectivity and lengths of join paths to measure their strengths. A novel approach is used to find the best join paths between relations, which converts the database schema into a graph, so that the shortest paths in the graph correspond to best join paths in the database. Ross uses a stream-based system architecture to handle complex queries containing logical operators, which can find the most related objects upon users’ requests. Comprehensive experiments are conducted to show the high scalability and effectiveness of Ross.

Searching for related objects in relational databases

Capturing application semantics and allowing a human analyst to express his focus in mining have been the motivation for several recent studies on constrained mining. In this paper, we introduce and study the problem of constrained clustering—finding clusters that satisfy certain user-specified constraints. We argue that this problem arises naturally in practice. Two types of constraints are discussed in this paper. The first type of constraints are imposed by physical obstacles that exist in the region of clustering. The second type of constraints are SQL constraints which every cluster must satisfy. We provide a prelimary introduction to both types of constraints and discuss some techniques for solving them.

Geo-spatial clustering with user-specified constraints

A Case-Based Reasoning Approach for Associative Query Answering

WINACS (Web-based Information Network Analysis for Computer Science) is a project that incorporates many recent, exciting developments in data sciences to construct a Web-based computer science information network and to discover, retrieve, rank, cluster, and analyze such an information network. With the rapid development of the Web, huge amounts of information are available in the form of Web documents, structures, and links. It has been a dream of the database and Web communities to harvest such information and reconcile the unstructured nature of the Web with the neat, semi-structured schemas of the database paradigm. Taking computer science as a dedicated domain, WINACS first discovers related Web entity structures, and then constructs a heterogeneous computer science information network in order to rank, cluster and analyze this network and support intelligent and analytical queries.

/pdf/winacs-construction-and-analysis-of-web-based-computer-58vbr2fuxu.pdf

WINACS: construction and analysis of web-based computer science information networks

In this paper we propose two novel approaches to enhance cross-lingual entity linking (CLEL). One is based on cross-lingual information networks, aligned based on monolingual information extraction, and the other uses topic modeling to ensure global consistency. We enhance a strong baseline system derived from a combination of state-of-the-art machine translation and monolingual entity linking to achieve 11.2% improvement in B-Cubed+ F-measure. Our system achieved highly competitive results in the NIST Text Analysis Conference (TAC) Knowledge Base Population (KBP2011) evaluation. We also provide detailed qualitative and quantitative analysis on the contributions of each approach and the remaining challenges.

/pdf/analysis-and-refinement-of-cross-lingual-entity-linking-4y0obf2lcv.pdf

Jiawei Han

Papers

Searching for related objects in relational databases

Geo-spatial clustering with user-specified constraints

A Case-Based Reasoning Approach for Associative Query Answering

WINACS: construction and analysis of web-based computer science information networks

Analysis and refinement of cross-lingual entity linking