Statistics for Spatial Data.

Concept drift refers to a non stationary learning problem over time. The training and the application data often mismatch in real life problems. In this report we present a context of concept drift problem 1. We focus on the issues relevant to adaptive training set formation. We present the framework and terminology, and formulate a global picture of concept drift learners design. We start with formalizing the framework for the concept drifting data in Section 1. In Section 2 we discuss the adaptivity mechanisms of the concept drift learners. In Section 3 we overview the principle mechanisms of concept drift learners. In this chapter we give a general picture of the available algorithms and categorize them based on their properties. Section 5 discusses the related research fields and Section 5 groups and presents major concept drift applications. This report is intended to give a bird's view of concept drift research field, provide a context of the research and position it within broad spectrum of research fields and applications.

Learning under Concept Drift: an Overview

Decision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. The most successful strategy for inducing decision trees is the greedy top-down recursive approach, which has been continuously improved by researchers over the past 40 years. In this paper, we propose a paradigm shift in the research of decision trees: instead of proposing a new manually designed method for inducing decision trees, we propose automatically designing decision-tree induction algorithms tailored to a specific type of classification data set (or application domain). Following recent breakthroughs in the automatic design of machine learning algorithms, we propose a hyper-heuristic evolutionary algorithm called hyper-heuristic evolutionary algorithm for designing decision-tree algorithms (HEAD-DT) that evolves design components of top-down decision-tree induction algorithms. By the end of the evolution, we expect HEAD-DT to generate a new and possibly better decision-tree algorithm for a given application domain. We perform extensive experiments in 35 real-world microarray gene expression data sets to assess the performance of HEAD-DT, and compare it with very well known decision-tree algorithms such as C4.5, CART, and REPTree. Results show that HEAD-DT is capable of generating algorithms that significantly outperform the baseline manually designed decision-tree algorithms regarding predictive accuracy and F-measure.

https://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/IEEE-TEC-2014-Barros-as-publ.pdf

Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets

Since the introduction of the Apriori algorithm a decade ago, the problem of mining patterns is becoming a very active research area, and efficient techniques have been widely applied to the problems either in industry or science. Currently, the data mining community is focusing on new problems such as: mining new kinds of patterns, mining patterns under constraints, considering new kinds of complex data, and real-world applications of these concepts. Data Mining Patterns: New Methods and Applications provides an overall view of the recent solutions for mining, and also explores new kinds of patterns. This book offers theoretical frameworks and presents challenges and their possible solutions concerning pattern extractions, emphasizing both research techniques and real-world applications. Data Mining Patterns: New Methods and Applications portrays research applications in data models, techniques and methodologies for mining patterns, multi-relational and multidimensional pattern mining, fuzzy data mining, data streaming, incremental mining, and many other topics.

Data Mining Patterns: New Methods and Applications

Using a Neonatal Intensive Care Unit (NICU) case study, this work investigates the current CRoss Industry Standard Process for Data Mining (CRISP-DM) approach for modeling Intelligent Data Analysis (IDA)-based systems that perform temporal data mining (TDM). The case study highlights the need for an extended CRISP-DM approach when modeling clinical systems applying Data Mining (DM) and Temporal Abstraction (TA). As the number of such integrated TA/DM systems continues to grow, this limitation becomes significant and motivated our proposal of an extended CRISP-DM methodology to support TDM, known as CRISP-TDM. This approach supports clinical investigations on multi-dimensional time series data. This research paper has three key objectives: 1) Present a summary of the extended CRISP-TDM methodology; 2) Demonstrate the applicability of the proposed model to the NICU data, focusing on the challenges associated with multi-dimensional time series data; and 3) Describe the proposed IDA architecture for applying integrated TDM.

Extending CRISP-DM to incorporate temporal data mining of multidimensional medical data streams: A neonatal intensive care unit case study

Both the temporal and spatial dimensions add substantial complexity to data mining tasks. First of all, the spatial relations, both metric (such as distance) and non-metric (such as topology, direction, shape, etc.) and the temporal relations (such as before and after) are information bearing and therefore need to be considered in the data mining methods. Secondly, some spatial and temporal relations are implicitly defined, that is, they are not explicitly encoded in a database. These relations must be extracted from the data and there is a trade-off between precomputing them before the actual mining process starts (eager approach) and computing them on-the-fly when they are actually needed (lazy approach). Moreover, despite much formalization of space and time relations available in spatio-temporal reasoning, the extraction of spatial/ temporal relations implicitly defined in the data introduces some degree of fuzziness that may have a large impact on the results of the data mining process. J Intell Inf Syst (2006) 27: 187–190 DOI 10.1007/s10844-006-9949-3

/pdf/mining-spatio-temporal-data-54ahzh5bp0.pdf

Mining spatio-temporal data

Ubiquitous Knowledge Discovery is a new research area at the intersection of machine learning and data mining with mobile and distributed systems. In this paper the main characteristics of the objects of study are defined and a high-level framework for analyzing ubiquitous knowledge discovery systems is introduced. Next, a number of examples from a broad range of application areas are reviewed and analyzed in terms of this framework. Based on this material, important characteristics of this field are identified and a number of research challenges are discussed. Ubiquitous Knowledge Discovery Knowledge Discovery in ubiquitous environments (KDUbiq) is an emerging area of research at the intersection of the two major challenges of highly distributed and mobile systems and advanced knowledge discovery systems. Today, in many subfields of computer science and engineering, being intelligent and adaptive marks the difference between a system that works in a complex and changing environment and a system that does not work. Hence, projects across many areas, ranging from Web 2.0 to ubiquitous computing and robotics, aim to create systems which are “smart”, “intelligent”, “adaptive” etc., allowing to solve problems that could not be solved before. A central assumption of KDUbiq is that what seems to be a bewildering array of different methodologies and approaches for building smart, adaptive, intelligent systems, can be cast into a coherent, integrated set of key ideas centered on the notion of learning from experience. Focusing on these key ideas, KDUbiq provides a unifying framework for systematically investigating the mutual dependencies of otherwise quite unrelated technologies employed in building next-generation intelligent systems: machine learning, data mining, sensor networks, grids, P2P, data stream mining, activity recognition, Web 2.0, privacy, user modeling and others. Machine learning and data mining emerge as basic methodologies and indispensable building blocks for some of the most difficult computer science and engineering challenges of the next decade. From a high-level perspective, key characteristics of an ubiquitous knowledge discovery application are: C1. Time and space. The objects of analysis exist in time and space. Often they are able to move. C2. Dynamic environment. These objects might not be stable over the life-time of an application. Instead they might appear or disappear. They exist in a dynamic and unstable environment, evolving incrementally over time. C3. Information processing capability. The objects are endowed with information processing capabilities. C4. Locality. The objects never see the global picture, knowing only their local spatio-temporal environment. C5. Real-Time. Because they typically have to take decisions or even act upon their environment, analysis and inference has to be done in real-time, and not only on historic data; the models have to evolve incrementally in correspondence with the evolving environment. C6. Distributed. In many cases the object will be able to exchange information with other objects, thus forming a truly distributed environment. Objects to which these characteristics apply are humans, animals, and, increasingly, various kinds of computing devices. It is the latter, that form the objects of study for KDUbiq. For analyzing the different possible architectures of ubiquitous knowledge discovery systems within a highlevel framework, we introduce six dimensions of KDUbiq: 1. Application Area. 2. Ubiquitous Technologies. 3. Resource Aware Algorithms. 4. Ubiquitous Data Collection. 5. Privacy and Security. 6. HCI and User-Modeling. When designing a ubiquitous knowledge discovery system, major design decisions in each of these six dimensions have to be taken. These choices are mutually constraining each other and dependencies among them have to be carefully analyzed. KDUbiq thus adopts a systems view on how to build next generation knowledge discovery systems. Two important aspects to be ubiquity have to be distinguished, namely • the ubiquity of data, and • ubiquity of computing. In a prototypical application the ubiquity of the environment corresponds naturally to the ubiquity of the data – e.g. the spatio-temporally tagged data in case study 3 arise because the vehicles are moving, in case study 5 they arise because the collections are owned by different people. But there are borderline cases that are ubiquitous in one way but not in the other, e.g. clusters or grids for speeding up data analysis by distributing files and computations to various computers, or track mining from GPS data where the data a analyzed on a central server in an offline batch setting. To stimulate research and further define the field, the KDUbiq research network (www.kdubiq.org), funded by the European Commission, was launched in 2006. Currently it has more than 40 members. It is organized around working groups for each of the dimensions of KDUbiq. It has launched workshops series at KDD, ICDM, and PKDD and ECML/PKDD, including mining data streams from sensor data, on privacy-preserving data mining, on spatial data mining, on user modeling, on ubiquitous web mining. The general points that emerge from these activities are discussed in a joint book, currently under preparation, the KDUbiq “Blueprint on Ubiquitous Knowledge Discovery”. It aims for a comprehensive overview on the six design dimensions and the research agenda needed for implementing the KDUbiq vision. To provide a more specific description of the content of KDUbiq, in this document we analyze a number of case studies (in this extended abstract the descriptions have to be shortened). The following selection criteria have been used: (1) each case study focuses on a different domain; (2) it presents a challenging real-life problem; (3) there is a body of prior technical work addressing at least some of the six dimensions of ubiquitous knowledge discovery. Existing work is not necessarily done under the label of “Ubiquitous Knowledge Discovery”. The subject is new and draws on work scattered around many communities. For a review of earlier work on distributed data mining see [7]. Case Study 1: Autonomous driving vehicle The first case study provides an impressive example how machine learning can help to solve an important real world task: The DARPA grand challenge. The goal was to develop an autonomous robot capable of traversing unrehearsed road-terrain. The robot had to navigate a 228 km long course through the Mojave desert in no more than 10 hours. The challenge was won in 2005 by the robot Stanley, built by a Stanford-based team lead by Sebastian Thrun [17]. Modern vehicles fit the basic characteristics of ubiquitous knowledge discovery systems very well: they exist in a dynamic environment, moving in time and space, equipped with sensors, increasingly communicating with other devices, e.g. satellites, for navigation. What sets Stanley apart from traditional cars on the hardware side is the large number of additional sensors, computational power and actuators. Stanley uses machine learning for a number of learning tasks, both offline and online. An offline classification task solved with machine learning is obstacle detection, where a first order Markov model is used. A second online task is road finding: classifying images into drivable and non-drivable areas. An adaptive Mixture of Gaussians algorithm is used to model a distribution that changes over time. It would be impossible to train the system offline for all possible situations. Case Study 2: Activity recognition – inferring transportation routines from GPS-data The widespread use of GPS devices led to an explosive interest in this type of data. One emerging area is assistive technologies: A personal guidance system helping cognitively impaired persons to find their way through a complex transportation system. This application has been proposed by the project Opportunity Knocks [14][11]. The basic infrastructure is a mobile device equipped with GPS and connected to a server. An inference module running on the server is able learn a person’s transportation routines from the GPS data collected. It is able to give advice to persons, which route to take or where to get off a bus, and it can warn the user if he commits errors, e.g. takes the wrong bus line. Machine learning algorithms are used to infer likely routes, activities, transportation destinations and deviations from a normal route. It is an unsupervised learning task. The basic knowledge representation mechanism is a Dynamic Bayesian Network. In further work, Conditional Random Fields are used. Case Study 3: Intelligent Multi-Agent Systems Smart Home MavHome [3] is a project that aims to build an intelligent environment, a smart home, which is able to acquire and apply knowledge about its inhabitants and surroundings. A home is seen as a rational agent capable of perceiving the state of the home through sensors an acting upon the environment through effectors. MavHome uses a sensor network for perceiving light, humidity, smoke, gas (CO), motion, and door, window seat status sensors. Inhabitant localization is done using passive infrared sensors. The software architecture is based on CORBA for communication between agents. The system is based on combining multiple heterogeneous machine learning algorithms in order to identify repeatable behaviors (patterns), to predict inhabitant activity and to learn a control strategy. The information is used for automation and optimization of the conditions in the house. For detecting patterns a sequential pattern mining algorithm ED is used which minimizes description length, and processes data as they arrive, thus assuming a data stream setting. Behavior prediction is done via the ALZ algorithm, taking ideas from the well-known LZ78 text compression algorithm. The predictive performance on real-world data collected over a month, was 44% when ALZ was combined wit

/pdf/research-challenges-in-ubiquitous-knowledge-discovery-2rzph9rn0q.pdf

Research Challenges in Ubiquitous Knowledge Discovery

Over the last years, ubiquitous computing has started to create a new world of small, heterogeneous, and distributed devices that have the ability to sense, to communicate and interact in ad hoc or sensor networks and peer2peer systems. These large scale distributed systems have in many cases to interact in real-time with their users. Knowledge Discovery in ubiquitous environments (KDubiq) is an emerging area of research at the intersection of the two major challenges of highly distributed and mobile systems and advanced knowledge discovery systems. It aims to provide a unifying framework for systematically investigating the mutual dependencies of otherwise quite unrelated technologies employed in building next-generation intelligent systems: machine learning, data mining, sensor networks, grids, P2P, data stream mining, activity recognition, Web 2.0, privacy, user modeling and others. In a fully ubiquitous setting, the learning typically takes place in situ, inside the small devices. Its characteristics are quite different from the current mainstream data mining and machine learning. Instead of offline-learning in a batch setting, sequential learning, anytime learning, real-time learning, online learning etc. under real-time constraints from ubiquitous and distributed data is needed. Instead of learning from stationary distributions, concept drift is the rule rather than the exception. Instead of large stand-alone workstations, learning takes place in unreliable, highly resource constrained environments in terms of battery power and bandwidth. The goal of this special issue is to promote an interdisciplinary forum for researchers who deal with sequential learning, anytime learning, real-time learning, online learning, etc. from ubiquitous and distributed data. Distributed Learning from Data Streams is a recent and increasing research area with challenging applications and contributions from fields like Data Bases, Data Mining, Machine Learning, and Statistics. The selected papers cover a large spectrum in the research of Ubiquitous Knowledge Discovery that goes from frequent pattern mining algorithms, distributed clustering, outlier detection to multi-relational learning The common concept in all the papers is that learning occurs while data continuously flows eventually produced from distributed sources. The first paper, Gama and Pereira presents a new distributed clustering algorithm which reduces both the dimensionality and the communication burden between sensors. Bifet and Gavalda propose new algorithms for adaptively mining closed rooted trees, both labeled and unlabeled, from data streams that change over time. Closed patterns are powerful representatives of frequent patterns, since they eliminate

Ubiquitous Knowledge Discovery

We may not be able to make you love reading, but ubiquitous knowledge discovery challenges techniques applications 1st edition will lead you to love reading starting from now. Book is the window to open the new world. The world that you want is in the better stage and level. World will always guide you to even the prestige stage of the life. You know, this is some of how reading will give you the kindness. In this case, more books you read more knowledge you know, but it can mean also the bore is full.

Ubiquitous knowledge discovery: challenges, techniques, applications

In the past, the development of machine learning approaches was to some extent motivated by the availability of data and increased computational power. Ubiquitous computing bears the promise of stimulating a similar leap forward. Small devices can now be installed in many places, mobile and wearable devices enable registration of large amounts of information, thus generating a wide range of new types of data for which new learning and discovery methods are needed, far beyond existing ones.

Introduction: the challenge of ubiquitous knowledge discovery

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