rEMM: Extensible Markov Model for Data Stream Clustering in R

Clustering streams of continuously arriving data has become an important application of data mining in recent years and efficient algorithms have been proposed by several researchers. However, clustering alone neglects the fact that data in a data stream is not only characterized by the proximity of data points which is used by clustering, but also by a temporal component. The extensible Markov model (EMM) adds the temporal component to data stream clustering by superimposing a dynamically adapting Markov chain. In this paper we introduce the implementation of the R extension package rEMM which implements EMM and we discuss some examples and applications.

rEMM: Extensible Markov Model for Data Stream Clustering in R

Clustering streams of continuously arriving data has become an important application of data mining in recent years and efficient algorithms have been proposed by several researchers. However, clustering alone neglects the fact that data in a data stream is not only characterized by the proximity of data points which is used by clustering, but also by a temporal component. The extensible Markov model (EMM) adds the temporal component to data stream clustering by superimposing a dynamically adapting Markov chain. In this paper we introduce the implementation of the <b>R</b> extension package <b>rEMM</b> which implements EMM and we discuss some examples and applications

Spacio-temporal situation assessment for mobile robots

In this paper, we present a framework for situation modeling and assessment for mobile robot applications. We consider situations as data patterns that characterize unique circumstances for the robot, and represented not only by the data but also its temporal and spacial sequence. Dynamic Markov chains are used to model the situation states and sequence, where stream clustering is used for state matching and dealing with noise. In experiments using simulated and real data, we show that we are able to learn a situation sequence for a mobile robot passing through a narrow passage. After learning the situation models we are able to robustly recognize and predict the situation

Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R

In recent years, data streams have become an increasingly important area of research for the computer science, database and statistics communities. Data streams are ordered and potentially unbounded sequences of data points created by a typically non-stationary data generating process. Common data mining tasks associated with data streams include clustering, classification and frequent pattern mining. New algorithms for these types of data are proposed regularly and it is important to evaluate them thoroughly under standardized conditions. In this paper we introduce stream, a research tool that includes modeling and simulating data streams as well as an extensible framework for implementing, interfacing and experimenting with algorithms for various data stream mining tasks. The main advantage of stream is that it seamlessly integrates with the large existing infrastructure provided by R. In addition to data handling, plotting and easy scripting capabilities, R also provides many existing algorithms and enables users to interface code written in many programming languages popular among data mining researchers (e.g., C/C++, Java and Python). In this paper we describe the architecture of stream and focus on its use for data stream clustering research. stream was implemented with extensibility in mind and will be extended in the future to cover additional data stream mining tasks like classification and frequent pattern mining

A Comprehensive Survey on Rare Event Prediction

Rare event prediction involves identifying and forecasting events with a low probability using machine learning and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the machine learning pipeline, i.e., from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistical and machine learning. This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, data processing, algorithmic approaches, and evaluation approaches. Specifically, we consider 73 datasets from different modalities (i.e., numerical, image, text, and audio), four major categories of data processing, five major algorithmic groupings, and two broader evaluation approaches. This paper aims to identify gaps in the current literature and highlight the challenges of predicting rare events. It also suggests potential research directions, which can help guide practitioners and researchers.Comment: 44 page

Autonome Situationserkennung im klinischen Umfeld

The goal of the research project ROREAS (Robotic Rehabilitation Assistant for Stroke Patients) is the development of a robotic rehabilitation assistant for the self-training of stroke patients. The self-training aims at improving the walking and orientation skill of the patient. It consists mainly of a goal oriented movement in an environment of a rehabilitation center. The self-training is mostly performed on the hallways connecting the patient’s rooms. Due to the structure of the building or objects staying in the hallways the lateral space is limited forming narrow passages. Moving in such a confined space imposes deadlocks in narrow passages. Since a polite and attentive navigation is an important requirement for an assistive robot, these deadlock situations must be recognized in advance to trigger a proactive reaction. In this master thesis an approach is presented for anticipating deadlock situations caused by narrow passages. In a nutshell, situations concerning narrow passages are captured by real-valued feature vectors. Basically, the features capture the structure of the environment along the robot's movement in terms of a possible narrow passage and the possible space conflicts caused by a person. As part of the features, the movement of the persons are predicted allowing the robot to forecast possible space conflicts resulting in the considered problematic situations. By grouping the feature vectors into classes representing the appropriate treatments of their corresponding situation, the recognition task becomes a classification problem. Thus, a classifier which solve this classification problem can be used to map each feature vector which represents a situation directly to the appropriate treatment. A linear support vector machine and a handcrafted decision tree is used as a classifier. The experimental evaluations show that the used features are good suitable for recognizing deadlock situations. The decision tree performed better on the dataset of this thesis as the linear support vector machine.Zusammenfassung: Im Rahmen des Forschungsprojektes ROREAS (Interaktiver robotischer Reha-Assistent für das Lauf- und Orientierungstraining von Patienten nach Schlaganfällen) soll ein robotischer Laufassistent für eine Klinikumgebung entwickelt werden. Der Roboter soll Schlaganfallpatienten bei ihrem eigenständigen Lauftraining unterstützend begleiten. Das Lauftraining wird hauptsächlich auf den Korridoren der Klinik ausgeführt. Auf diesen Korridoren können Engstellen auftreten. Wenn der Roboter und eine Person sich gleichzeitig durch eine Engstelle bewegen, kann dies zu Verklemmungssituationen führen. Zur Gewährleistung einer höflichen und nutzerzentrierten Navigation, die ein wichtiger Bestandteil für die soziale Akzeptanz des Roboters ist, müssen Verklemmungssituationen im Voraus erkannt werden, um rechtzeitig eine Behandlung einzuleiten. Ziel der vorliegenden Masterarbeit ist die Entwicklung eines Verfahren zur vorausschauenden Erkennung von Verklemmungssituationen. Zur Erkennung von Verklemmungen werden Engstellensituationen durch reellwertige Merkmalsvektoren beschrieben. Diese erfassen die Platzkonflikte des Roboters mit einer Person in Bezug auf erkannte Engstellen. Als Teil der Merkmale wird die Bewegung von Personen prädiziert, um Platzkonflikte vorherzusagen, die möglicherweise zu Verklemmungen führen können. Indem die Merkmalsvektoren zu Klassen zusammengefasst werden, welche die Behandlung ihrer entsprechenden Situation repräsentiert, kann die Erkennung von Verklemmungssituationen als Klassifikationsproblem betrachtet werden. Folglich kann ein Klassifikator eingesetzt werden, um die Situationen direkt auf ihre Behandlung abzubilden. Als Klassifikatoren wurden in dieser Arbeit eine lineare Support Vektor Maschine und ein manuell designter Entscheidungsbaum genutzt. Die experimentellen Untersuchungen dieser Arbeit zeigen, dass der gewählte Merkmalsraum geeignet ist, um Verklemmungssituationen zu erkennen. Der Entscheidungsbaum erzielte dabei auf den Datensätzen dieser Arbeit eine bessere Erkennungsrate als die lineare Support Vektor Maschine.Ilmenau, Techn. Univ., Masterarbeit, 201