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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.

The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.

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Data Mining: Concepts and Techniques (2nd edition)

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.

Big Data: A Survey

Conventional Content-Based Image Retrieval (CBIR) systems make use of similarity measures estimated directly from low-level image features, involving multidimensional and exhaustive, nearest neighbor searching. In this paper we present an image retrieval methodology suited for efficient search in cultural heritage images that utilizes similarity measures defined over higher-level patterns associated with clusters of low-level image features. The similarity between two patterns is estimated as a function of the similarity between both the structure and the measure components of the patterns. We evaluate our system using cultural heritage images derived from the repository of the Foundation of Hellenic World (FHW). For the experiments we use Local Binary Pattern (LBP) and contrast distributions as image features and the results show that the proposed pattern-based approach efficiently retrieves images compared to commonly employed methods.

/pdf/pattern-based-retrieval-of-cultural-heritage-images-39gt1hw6iy.pdf

Pattern-Based Retrieval of Cultural Heritage Images

This demo presents Pattern-Miner, an integrated environment for pattern management and mining that deals with the whole lifecycle of patterns, from their generation (using data mining techniques) to their storage and querying, putting also emphasis on the comparison between patterns and meta-mining operations over the extracted patterns. Pattern comparison (comparing results of the data mining process) and meta-mining are high level pattern operations that can be applied in a variety of applications, from database change management to image comparison and retrieval.

http://infolab.cs.unipi.gr/pubs/confs/kdd2008-pbms.pdf

Pattern-Miner: integrated management and mining over data mining models

Existing approaches for privacy-aware mobility data sharing aim at publishing an anonymized version of the mobility dataset, operating under the assumption that most of the information in the original dataset can be disclosed without causing any privacy violations. In this paper, we assume that the majority of the information that exists in the mobility dataset must remain private and the data has to stay in-house to the hosting organization. To facilitate privacy-aware sharing of the mobility data we develop a trajectory query engine that allows subscribed users to gain restricted access to the database to accomplish various analytic tasks. The proposed engine (i) audits queries for trajectory data to block potential attacks to user privacy, (ii) supports range, distance, and k-nearest neighbor spatial and spatiotemporal queries, and (iii) preserves user anonymity in answers to queries by (a) returning a set of carefully crafted, realistic fakes trajectories, and (b) ensuring that no user-specific sensitive locations are reported as part of the returned trajectories. Along this direction, we also present Private-HERMES, a platform that provides a two-dimension benchmark framework that includes: (i) the aforementioned privacy-aware trajectory query engine and (ii) a progressive analysis framework, which, apart from anonymization methods for data publishing, includes various well-known mobility data mining techniques to evaluate the effect of anonymization in the querying and mining results.

/pdf/a-framework-for-mobility-pattern-mining-and-privacy-aware-4uyh7junih.pdf

A Framework for Mobility Pattern Mining and Privacy- Aware Querying of Trajectory Data

The vast spread of GPS equipped mobile devices, such as smart phones, GPS navigation devices etc, combined with the development of appropriate techniques for storing, processing, querying, and mining such kind of data, has resulted in the production of huge amounts of location-aware information However, getting this kind of raw data into a meaningful form in terms of mobility is not a straightforward task at all During this transformation procedure, many aspects arise, such as the treatment of inaccurate or noisy information, the identification of trajectories as sequences of sampled positions, the reduction of the size of the datasets in order to deal with the storage challenges that may appear, etc Furthermore, in order to be able to evaluate the performance of spatiotemporal algorithms and data structures, enormous amounts of real-world mobility data are required, which cannot be easily found available out there To bridge this gap, many generators of moving object trajectories have been developed This chapter provides a review on the above-mentioned research field and is organized as follows After a necessary discussion on mobility data modeling (the concepts of time-stamped locations, trajectories, etc), we refresh our discussion on collecting raw mobility data through GPS devices and handle aspects that arise, such as noise and inaccuracy Then, we introduce the trajectory reconstruction problem and the most popular techniques dealing with this issue (trajectory identification, map-matching, compression) We conclude by familiarizing the reader with the notion of mobility data generators along with a discussion on several developments on that field, for movement either in free space or under network constraints

Modeling and Acquiring Mobility Data

In this paper we present an overview of the intelligent multimedia annotation and search system MetaOn. The core objective is to construct and integrate semantically rich metadata, extracted from documents and images, to facilitate intelligent search and analysis. The proposed MetaOn framework involves, ontology-based information extraction and data mining, semi-automatic construction of domain specific ontologies, content-based image indexing and retrieval, and metadata management. The Hellenic history has been chosen as a challenging application case study.

Yannis Theodoridis

Papers

Pattern-Based Retrieval of Cultural Heritage Images

Pattern-Miner: integrated management and mining over data mining models

A Framework for Mobility Pattern Mining and Privacy- Aware Querying of Trajectory Data

Modeling and Acquiring Mobility Data

Multimedia Annotation System for Intelligent Search