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

We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is generated as a mixture of topics, where the continuous-valued mixture proportions are distributed as a latent Dirichlet random variable. Inference and learning are carried out efficiently via variational algorithms. We present empirical results on applications of this model to problems in text modeling, collaborative filtering, and text classification.

/pdf/latent-dirichlet-allocation-rkwbqihm3p.pdf

Latent Dirichlet Allocation

Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu.

/pdf/ultrafast-and-memory-efficient-alignment-of-short-dna-3mrm42jmnc.pdf

Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

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

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

Unbalanced multiway trees are generally impractical external data structures because of their poor space performance. In order to avoid this handicap, we have adapted the technique of partial expansions to these trees. Compared to partially expandedB+-trees in terms of average performance, the method proposed is faster, has more compact indexes, and shows the same almostoptimal asymptotic space performance at the data bucket level.

Unbalanced multiway trees improved by partial expansions

We revisit the use of Genetic Programming (GP) to learn ranking functions in the context of web documents, by adding linking information. Our results show that GP can cope with larger sets of features as well as bigger document collections, obtaining small improvements over the state-of-the-art of GP learned functions applied to web search.

Learning Ranking Functions by Genetic Programming Revisited

Understanding Web Navigational Data is a crucial task for Web analysts as it may influence the website improvement process. However, major Web analytics tools do not provide enough insight, taking into account the vast amount of data available in Web servers’ log files. Moreover, some analysts argue that those tools have a lack of rich visualisations that enable the exploration of such data. In this paper we introduce new techniques applied to a highly interactive and exploratory tool, to allow drilling down through Web usage data. The system uses a set of coordinated visual abstractions from website structure and users’ navigation to provide different perspectives and hence, to assist the conversion of data into knowledge.

New Techniques for Visualising Web Navigational Data

We present a visual tool based in XQuery for querying and exploring XML documents. The tool is based in a simple but effective visual metaphor and a results visualization technique based in fisheye Trees. The application fulfills the expectations of facilitating the manipulation of XML documents for inexperienced users, offering original interaction features, improving upon similar work. As XML is one of the main metalanguages of the Web, this tool may have a broad impact.

A Visual Tool for Querying and Exploring XML Data

Introduction and Motivation The first problem we have with our field is what name to give it. Are we talking about Computer Science and/or Computer Engineering or Informatics? Is the correct term computer, computation, computational or informatics? Where do information systems fit in or should they be they considered as a separate area? I still do not have any clear answers. Perhaps the problem is intrinsic. As the joke goes: Computer Science has two problems: Computer and Science. Have you ever heard of the science of washing machines or any other machine? Do mathematics or physics need to say they are sciences? In short it’s a problem of coming of age, of maturity, and consequently of insecurity. In any case it is clear that what we do is rooted both in engineering and basic science. Before we go on it is necessary to make something clear. Many of the things I say here are self-evident or common sense. Nevertheless, despite being obvious, many of them have not been said by anybody else and that’s why I am saying them here. Is it that they are very obvious or that they only become obvious once you know them? The ultimate aim of any software is to transmit some knowledge to the mind of the person using it and vice versa. The biggest bottleneck occurs at interfaces, at the final point of communication with the user, and the problem lies not only in bandwidth but also in the very way information is represented (Figure 1). As Djikstra said recently, we have still not been able to rise the challenge of making large software systems less complex. How to eliminate this bottleneck is the main aim of this article, which may seem like a hotchpotch of apparently unconnected arguments. However, we often forget to analyse our universe as a whole, from the point of view of an outside observer. For anything we wish to study, form and content should be given their due degree of importance and consideration. I will start by presenting an analysis of the relationship between technology and culture, and an analysis of computing itself and in the context of our profession. I will go on to talk 1

/pdf/let-s-design-everything-again-thoughts-on-computing-and-its-3gpw12wy7j.pdf

Let's Design Everything Again: Thoughts on Computing and Its Teaching

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