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A multi-clustering fusion method is presented based on combining several runs of a clustering algorithm resulting in a common partition. More specifically, the results of several independent runs of the same clustering algorithm are appropriately combined to obtain a distinct partition of the data which is not affected by initialization and overcomes the instabilities of clustering methods. Subsequently, a fusion procedure is applied to the clusters generated during the previous 1 phase todoi:10.1142/s0129065705000360 pmid:16278943 fatcat:w746l3vqy5d5pfof4oeapjheoe
more »... mine the optimal number of clusters in the data set according to some predefined criteria.
The rising Data Web has brought forth the requirement to treat information as dynamically evolving aggregations of data from remote and heterogeneous sources, creating the need for intelligent management of the changedriven aspects of the underlying evolving entities. Datasets change in multiple levels, such as evolving semantics, as well as structural characteristics, such as their model and format. In this paper, we present an archiving system that is driven by the need to treat evolution indblp:conf/esws/MeimarisPP15 fatcat:7gh5trs6kfhhvc54bsbvbhdloq
more »... unified way, taking into account change management, provenance, temporality, and querying, and we implement the archive based on a data model and query language designed specifically for addressing preservation and evolution management in the Data Web.
Lecture Notes in Computer Science
Memory base learning is one of main fields in the area of machine learning. We propose a new methodology for addressing the classification task that relies on the main idea of the k -nearest neighbors algorithm, which is the most important representative of this field. In the proposed approach, given an unclassified pattern, a set of neighboring patterns is found, but not necessarily using all input feature dimensions. Also, following the concept of the naïve Bayesian classifier, we adopt thedoi:10.1007/11840930_40 fatcat:za4p25ovdjedde4d3wswr4zum4
more »... pothesis of the independence of input features in the outcome of the classification task. The two concepts are merged in an attempt to take advantage of their good performance features. In order to further improve the performance of our approach, we propose a novel weighting scheme of the memory base. Using the self-organizing maps model during the execution of the algorithm, dynamic weights of the memory base patterns are produced. Experimental results have shown superior performance of the proposed method in comparison with the aforementioned algorithms and their variations.
Lecture Notes in Computer Science
Present work introduces a probabilistic recognition scheme for hand gestures. Self organizing feature maps are used to model spatiotemporal information extracted through image processing. Two models are built for each gesture category and, along with appropriate distance metrics, produce a validated classification mechanism that performs consistently during experiments on acted gestures video sequences.doi:10.1007/978-3-540-74695-9_27 fatcat:gi5t5fqktjcpjaya457d4wvjh4
The web of data has brought forth the need to preserve and sustain evolving information within linked datasets; however, a basic requirement of data preservation is the maintenance of the datasets' structural characteristics as well. As open data are often found using different and/or heterogeneous data models and schemata from one source to another, there is a need to reconcile these mismatches and provide common denominations of interpretation on a multitude of levels, in order to be able toarXiv:1504.06451v2 fatcat:t3dvjwcs3zdpbm6mm7ua4ul7kq
more »... reserve and manage the evolution of the generated resources. In this paper, we present a linked data approach for the preservation and archiving of open heterogeneous datasets that evolve through time, at both the structural and the semantic layer. We first propose a set of re-quirements for modelling evolving linked datasets. We then proceed on concep-tualizing a modelling framework for evolving entities and place these in a 2x2 model space that consists of the semantic and the temporal dimensions.
This work presents the design and experimental verification of an original system architecture aiming at recognizing gestures based solely on the hand trajectory. Self organizing feature maps are used to model spatial information while Markov models encode the temporal aspect of hand position within a trajectory. A validated classification mechanism is produced through a set of models and a committee machine setup ensures robustness as indicated by the experimental results performed.doi:10.1109/icme.2008.4607632 dblp:conf/icmcs/CaridakisKPDSK08 fatcat:gfrp5vazqbhdlkqkjyv54vhk54
The Data Web refers to the vast and rapidly increasing quantity of scientific, corporate, government and crowd-sourced data published in the form of Linked Open Data, which encourages the uniform representation of heterogeneous data items on the web and the creation of links between them. The growing availability of open linked datasets has brought forth significant new challenges regarding their proper preservation and the management of evolving information within them. In this paper, we focusarXiv:1504.01891v3 fatcat:wazigr2bsnamve3wpo2avfnzq4
more »... on the evolution and preservation challenges related to publishing and preserving evolving linked data across time. We discuss the main problems regarding their proper modelling and querying and provide a conceptual model and a query language for modelling and retrieving evolving data along with changes affecting them. We present in details the syntax of the query language and demonstrate its functionality over a real-world use case of evolving linked dataset from the biological domain.
In particular we would like to thank Christos Pateritsas, Yannis Roussakis, Hasapis Panagiotis, Marios Meimaris and Giorgos Flouris for their contribution to components of the DIACHRON platform utilized ...dblp:conf/edbt/VrousgouBPJ16 fatcat:hozkkp2fgncdrimvghcse3q7de