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

Data Mining - Concepts and Techniques.

How to Do Things With Words

Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

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Survey of clustering algorithms

One of the most considerable functions in a hospital's infection control program is the surveillance of antibiotic resistance. Several traditional methods used to measure it do not provide adequate and promising results for further analysis. Data mining techniques, such as the association rules, have been used in the past and successfully led to discovering interesting patterns in public health data. In this work, we present the architecture of a novel framework which integrates data from multiple hospitals, discovers association rules, stores them in a data warehouse for future analysis and provides anytime accessibility through an intuitive Web interface. We implemented the proposed architecture as a Web application and evaluated it using data from the WHONET software installed in many Greek hospitals that belong to "the Greek System for Surveillance of Antimicrobial Resistance" network. The contribution of the proposed framework is considered to be a standardized workflow aiming at the integration of data produced by various hospitals into a consistent data warehouse and the use of a mechanism that detects hidden and previously unknown patterns on large datasets, in terms of association rules, which can provide surveillance warnings.

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A Large Scale Data Mining Approach to Antibiotic Resistance Surveillance

The advent of digital libraries along with the tremendous growth of digital content call for distributed and scalable approaches for managing vast data collections. Peer-to-peer (P2P) networks emerge as a promising solution to delve with these challenges. However, the lack of global content/topology knowledge in an unstructured P2P system demands unsupervised methods for content organization and necessitates efficient and high quality search mechanisms. Towards this end, Semantic Overlay Networks (SONs) have been proposed in the literature, and in this paper, an unsupervised method for decentralized and distributed generation of SONs, called DESENT, is proposed. We prove the feasibility of our approach through analytical cost models and we show through simulations that, when compared to flooding, our approach improves recall by more than 3-10 times, depending on the network topology.

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Scalable semantic overlay generation for p2p-based digital libraries

Online advertising is a fast developing industry - in 2011 its revenues reached $31 billion. Paid search marketing is extremely competitive while online advertising campaign creation and development are very demanding in terms of time and expert human resources. Assisting or even automating the work of an advertising specialist has emerged as a requirement for companies and research institutes over the last few years, mainly because of the commercial value of this endeavour. In this context, we developed GrammAds, an automated keyword and ad creative generator. This system generates multiword keywords (n-grams) and automated ad creative recommendations, while it organizes properly the campaigns which are finally uploaded to the auctioneer platform and start running. In this paper, we analyze the proposed methodology and we also present the main functionality of the GrammAds application along with an experimental evaluation.

http://www.db-net.aueb.gr/matina/docs/dedm2013_thomaidou.pdf

GrammAds: Keyword and Ad Creative Generator for Online Advertising Campaigns

Mobile terminals (cellular phones, PDAs, palmtops etc.) emerge as a new class of small-scale, ad-hoc service providers that share data and functionality via mobile web services' calls. In mobile service discovery, it is often the case that implicit contextual information such as the location of the requestor, time the query was submitted, capabilities of the involved devices have a significant impact on query evaluation and the quality of the results. On the other hand, lack of scalability and the well-known single point of failure problem call for distribution of service directories into several peers. In this paper, we present an approach for context-aware service discovery where service directories reside in a P2P architecture. We explore the role and benefits of context-awareness and caching query results in the service discovery process. We conduct extensive experiments coming up with guidelines for service directory design in a P2P context.

http://www.db-net.aueb.gr/files/mdm05.pdf

The role of caching and context-awareness in P2P service discovery

Graphs have been widely used as modeling tools in Natural Language Processing (NLP), Text Mining (TM) and Information Retrieval (IR). Traditionally, the unigram bag-of-words representation is applied; that way, a document is represented as a multiset of its terms, disregarding dependencies between the terms. Although several variants and extensions of this modeling approach have been proposed, the main weakness comes from the underlying term independence assumption; the order of the terms within a document is completely disregarded and any relationship between terms is not taken into account in the final task. To deal with this problem, the research community has explored various representations, and to this direction, graphs constitute a well-developed model for text representation. The goal of this tutorial is to offer a comprehensive presentation of recent methods that rely on graph-based text representations to deal with various tasks in Text Mining, NLP and IR.

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Michalis Vazirgiannis

Papers

A Large Scale Data Mining Approach to Antibiotic Resistance Surveillance

Scalable semantic overlay generation for p2p-based digital libraries

GrammAds: Keyword and Ad Creative Generator for Online Advertising Campaigns

The role of caching and context-awareness in P2P service discovery

GraphRep: Boosting Text Mining, NLP and Information Retrieval with Graphs