379 research outputs found

    Centrality measures and analyzing dot-product graphs

    Full text link
    In this thesis we investigate two topics in data mining on graphs; in the first part we investigate the notion of centrality in graphs, in the second part we look at reconstructing graphs from aggregate information. In many graph related problems the goal is to rank nodes based on an importance score. This score is in general referred to as node centrality. In Part I. we start by giving a novel and more efficient algorithm for computing betweenness centrality. In many applications not an individual node but rather a set of nodes is chosen to perform some task. We generalize the notion of centrality to groups of nodes. While group centrality was first formally defined by Everett and Borgatti (1999), we are the first to pose it as a combinatorial optimization problem; find a group of k nodes with largest centrality. We give an algorithm for solving this optimization problem for a general notion of centrality that subsumes various instantiations of centrality that find paths in the graph. We prove that this problem is NP-hard for specific centrality definitions and we provide a universal algorithm for this problem that can be modified to optimize the specific measures. We also investigate the problem of increasing node centrality by adding or deleting edges in the graph. We conclude this part by solving the optimization problem for two specific applications; one for minimizing redundancy in information propagation networks and one for optimizing the expected number of interceptions of a group in a random navigational network. In the second part of the thesis we investigate what we can infer about a bipartite graph if only some aggregate information -- the number of common neighbors among each pair of nodes -- is given. First, we observe that the given data is equivalent to the dot-product of the adjacency vectors of each node. Based on this knowledge we develop an algorithm that is based on SVD-decomposition, that is capable of almost perfectly reconstructing graphs from such neighborhood data. We investigate two versions of this problem, in the versions the dot-product of nodes with themselves, e.g. the node degrees, are either known or hidden

    Data mining Twitter for cancer, diabetes, and asthma insights

    Get PDF
    Twitter may be a data resource to support healthcare research. Literature is still limited related to the potential of Twitter data as it relates to healthcare. The purpose of this study was to contrast the processes by which a large collection of unstructured disease-related tweets could be converted into structured data to be further analyzed. This was done with the objective of gaining insights into the content and behavioral patterns associated with disease-specific communications on Twitter. Twelve months of Twitter data related to cancer, diabetes, and asthma were collected to form a baseline dataset containing over 34 million tweets. As Twitter data in its raw form would have been difficult to manage, three separate data reduction methods were contrasted to identify a method to generate analysis files, maximizing classification precision and data retention. Each of the disease files were then run through a CHAID (chi-square automatic interaction detector) analysis to demonstrate how user behavior insights vary by disease. Chi-square Automatic Interaction Detector (CHAID) was a technique created by Gordon V. Kass in 1980. CHAID is a tool used to discover the relationship between variables. This study followed the standard CRISP-DM data mining approach and demonstrates how the practice of mining Twitter data fits into this six-stage iterative framework. The study produced insights that provide a new lens into the potential Twitter data has as a valuable healthcare data source as well as the nuances involved in working with the data

    Predictive Accuracy of Recommender Algorithms

    Get PDF
    Recommender systems present a customized list of items based upon user or item characteristics with the objective of reducing a large number of possible choices to a smaller ranked set most likely to appeal to the user. A variety of algorithms for recommender systems have been developed and refined including applications of deep learning neural networks. Recent research reports point to a need to perform carefully controlled experiments to gain insights about the relative accuracy of different recommender algorithms, because studies evaluating different methods have not used a common set of benchmark data sets, baseline models, and evaluation metrics. The dissertation used publicly available sources of ratings data with a suite of three conventional recommender algorithms and two deep learning (DL) algorithms in controlled experiments to assess their comparative accuracy. Results for the non-DL algorithms conformed well to published results and benchmarks. The two DL algorithms did not perform as well and illuminated known challenges implementing DL recommender algorithms as reported in the literature. Model overfitting is discussed as a potential explanation for the weaker performance of the DL algorithms and several regularization strategies are reviewed as possible approaches to improve predictive error. Findings justify the need for further research in the use of deep learning models for recommender systems

    Content Recommendation Through Linked Data

    Get PDF
    Nowadays, people can easily obtain a huge amount of information from the Web, but often they have no criteria to discern it. This issue is known as information overload. Recommender systems are software tools to suggest interesting items to users and can help them to deal with a vast amount of information. Linked Data is a set of best practices to publish data on the Web, and it is the basis of the Web of Data, an interconnected global dataspace. This thesis discusses how to discover information useful for the user from the vast amount of structured data, and notably Linked Data available on the Web. The work addresses this issue by considering three research questions: how to exploit existing relationships between resources published on the Web to provide recommendations to users; how to represent the user and his context to generate better recommendations for the current situation; and how to effectively visualize the recommended resources and their relationships. To address the first question, the thesis proposes a new algorithm based on Linked Data which exploits existing relationships between resources to recommend related resources. The algorithm was integrated into a framework to deploy and evaluate Linked Data based recommendation algorithms. In fact, a related problem is how to compare them and how to evaluate their performance when applied to a given dataset. The user evaluation showed that our algorithm improves the rate of new recommendations, while maintaining a satisfying prediction accuracy. To represent the user and their context, this thesis presents the Recommender System Context ontology, which is exploited in a new context-aware approach that can be used with existing recommendation algorithms. The evaluation showed that this method can significantly improve the prediction accuracy. As regards the problem of effectively visualizing the recommended resources and their relationships, this thesis proposes a visualization framework for DBpedia (the Linked Data version of Wikipedia) and mobile devices, which is designed to be extended to other datasets. In summary, this thesis shows how it is possible to exploit structured data available on the Web to recommend useful resources to users. Linked Data were successfully exploited in recommender systems. Various proposed approaches were implemented and applied to use cases of Telecom Italia

    PERSONALIZED POINT OF INTEREST RECOMMENDATIONS WITH PRIVACY-PRESERVING TECHNIQUES

    Get PDF
    Location-based services (LBS) have become increasingly popular, with millions of people using mobile devices to access information about nearby points of interest (POIs). Personalized POI recommender systems have been developed to assist users in discovering and navigating these POIs. However, these systems typically require large amounts of user data, including location history and preferences, to provide personalized recommendations. The collection and use of such data can pose significant privacy concerns. This dissertation proposes a privacy-preserving approach to POI recommendations that address these privacy concerns. The proposed approach uses clustering, tabular generative adversarial networks, and differential privacy to generate synthetic user data, allowing for personalized recommendations without revealing individual user data. Specifically, the approach clusters users based on their fuzzy locations, generates synthetic user data using a tabular generative adversarial network and perturbs user data with differential privacy before it is used for recommendation. The proposed approaches achieve well-balanced trade-offs between accuracy and privacy preservation and can be applied to different recommender systems. The approach is evaluated through extensive experiments on real-world POI datasets, demonstrating that it is effective in providing personalized recommendations while preserving user privacy. The results show that the proposed approach achieves comparable accuracy to traditional POI recommender systems that do not consider privacy while providing significant privacy guarantees for users. The research\u27s contribution is twofold: it compares different methods for synthesizing user data specifically for POI recommender systems and offers a general privacy-preserving framework for different recommender systems. The proposed approach provides a novel solution to the privacy concerns of POI recommender systems, contributes to the development of more trustworthy and user-friendly LBS applications, and can enhance the trust of users in these systems

    Perception of Unstructured Environments for Autonomous Off-Road Vehicles

    Get PDF
    Autonome Fahrzeuge benötigen die FĂ€higkeit zur Perzeption als eine notwendige Voraussetzung fĂŒr eine kontrollierbare und sichere Interaktion, um ihre Umgebung wahrzunehmen und zu verstehen. Perzeption fĂŒr strukturierte Innen- und Außenumgebungen deckt wirtschaftlich lukrative Bereiche, wie den autonomen Personentransport oder die Industrierobotik ab, wĂ€hrend die Perzeption unstrukturierter Umgebungen im Forschungsfeld der Umgebungswahrnehmung stark unterreprĂ€sentiert ist. Die analysierten unstrukturierten Umgebungen stellen eine besondere Herausforderung dar, da die vorhandenen, natĂŒrlichen und gewachsenen Geometrien meist keine homogene Struktur aufweisen und Ă€hnliche Texturen sowie schwer zu trennende Objekte dominieren. Dies erschwert die Erfassung dieser Umgebungen und deren Interpretation, sodass Perzeptionsmethoden speziell fĂŒr diesen Anwendungsbereich konzipiert und optimiert werden mĂŒssen. In dieser Dissertation werden neuartige und optimierte Perzeptionsmethoden fĂŒr unstrukturierte Umgebungen vorgeschlagen und in einer ganzheitlichen, dreistufigen Pipeline fĂŒr autonome GelĂ€ndefahrzeuge kombiniert: Low-Level-, Mid-Level- und High-Level-Perzeption. Die vorgeschlagenen klassischen Methoden und maschinellen Lernmethoden (ML) zur Perzeption bzw.~Wahrnehmung ergĂ€nzen sich gegenseitig. DarĂŒber hinaus ermöglicht die Kombination von Perzeptions- und Validierungsmethoden fĂŒr jede Ebene eine zuverlĂ€ssige Wahrnehmung der möglicherweise unbekannten Umgebung, wobei lose und eng gekoppelte Validierungsmethoden kombiniert werden, um eine ausreichende, aber flexible Bewertung der vorgeschlagenen Perzeptionsmethoden zu gewĂ€hrleisten. Alle Methoden wurden als einzelne Module innerhalb der in dieser Arbeit vorgeschlagenen Perzeptions- und Validierungspipeline entwickelt, und ihre flexible Kombination ermöglicht verschiedene Pipelinedesigns fĂŒr eine Vielzahl von GelĂ€ndefahrzeugen und AnwendungsfĂ€llen je nach Bedarf. Low-Level-Perzeption gewĂ€hrleistet eine eng gekoppelte Konfidenzbewertung fĂŒr rohe 2D- und 3D-Sensordaten, um SensorausfĂ€lle zu erkennen und eine ausreichende Genauigkeit der Sensordaten zu gewĂ€hrleisten. DarĂŒber hinaus werden neuartige Kalibrierungs- und RegistrierungsansĂ€tze fĂŒr Multisensorsysteme in der Perzeption vorgestellt, welche lediglich die Struktur der Umgebung nutzen, um die erfassten Sensordaten zu registrieren: ein halbautomatischer Registrierungsansatz zur Registrierung mehrerer 3D~Light Detection and Ranging (LiDAR) Sensoren und ein vertrauensbasiertes Framework, welches verschiedene Registrierungsmethoden kombiniert und die Registrierung verschiedener Sensoren mit unterschiedlichen Messprinzipien ermöglicht. Dabei validiert die Kombination mehrerer Registrierungsmethoden die Registrierungsergebnisse in einer eng gekoppelten Weise. Mid-Level-Perzeption ermöglicht die 3D-Rekonstruktion unstrukturierter Umgebungen mit zwei Verfahren zur SchĂ€tzung der DisparitĂ€t von Stereobildern: ein klassisches, korrelationsbasiertes Verfahren fĂŒr Hyperspektralbilder, welches eine begrenzte Menge an Test- und Validierungsdaten erfordert, und ein zweites Verfahren, welches die DisparitĂ€t aus Graustufenbildern mit neuronalen Faltungsnetzen (CNNs) schĂ€tzt. Neuartige DisparitĂ€tsfehlermetriken und eine Evaluierungs-Toolbox fĂŒr die 3D-Rekonstruktion von Stereobildern ergĂ€nzen die vorgeschlagenen Methoden zur DisparitĂ€tsschĂ€tzung aus Stereobildern und ermöglichen deren lose gekoppelte Validierung. High-Level-Perzeption konzentriert sich auf die Interpretation von einzelnen 3D-Punktwolken zur Befahrbarkeitsanalyse, Objekterkennung und Hindernisvermeidung. Eine DomĂ€nentransferanalyse fĂŒr State-of-the-art-Methoden zur semantischen 3D-Segmentierung liefert Empfehlungen fĂŒr eine möglichst exakte Segmentierung in neuen ZieldomĂ€nen ohne eine Generierung neuer Trainingsdaten. Der vorgestellte Trainingsansatz fĂŒr 3D-Segmentierungsverfahren mit CNNs kann die benötigte Menge an Trainingsdaten weiter reduzieren. Methoden zur ErklĂ€rbarkeit kĂŒnstlicher Intelligenz vor und nach der Modellierung ermöglichen eine lose gekoppelte Validierung der vorgeschlagenen High-Level-Methoden mit Datensatzbewertung und modellunabhĂ€ngigen ErklĂ€rungen fĂŒr CNN-Vorhersagen. Altlastensanierung und MilitĂ€rlogistik sind die beiden HauptanwendungsfĂ€lle in unstrukturierten Umgebungen, welche in dieser Arbeit behandelt werden. Diese Anwendungsszenarien zeigen auch, wie die LĂŒcke zwischen der Entwicklung einzelner Methoden und ihrer Integration in die Verarbeitungskette fĂŒr autonome GelĂ€ndefahrzeuge mit Lokalisierung, Kartierung, Planung und Steuerung geschlossen werden kann. Zusammenfassend lĂ€sst sich sagen, dass die vorgeschlagene Pipeline flexible Perzeptionslösungen fĂŒr autonome GelĂ€ndefahrzeuge bietet und die begleitende Validierung eine exakte und vertrauenswĂŒrdige Perzeption unstrukturierter Umgebungen gewĂ€hrleistet
    • 

    corecore