Fast, Linear Time, m-Adic Hierarchical Clustering for Search and Retrieval using the Baire Metric, with linkages to Generalized Ultrametrics, Hashing, Formal Concept Analysis, and Precision of Data Measurement

We describe many vantage points on the Baire metric and its use in clustering data, or its use in preprocessing and structuring data in order to support search and retrieval operations. In some cases, we proceed directly to clusters and do not directly determine the distances. We show how a hierarchical clustering can be read directly from one pass through the data. We offer insights also on practical implications of precision of data measurement. As a mechanism for treating multidimensional data, including very high dimensional data, we use random projections.Comment: 17 pages, 45 citations, 2 figure

Computational analysis of gene expression space associated with metastatic cancer

<p>Abstract</p> <p>Background</p> <p>Prostate carcinoma is among the most common types of cancer affecting hundreds of thousands people every year. Once the metastatic form of prostate carcinoma is documented, the majority of patients die from their tumors as opposed to other causes. The key to successful treatment is in the earliest possible diagnosis, as well as understanding the molecular mechanisms of metastatic progression. A number of recent studies have identified multiple biomarkers for metastatic progression. However, most of the studies consider only direct comparison between metastatic and non-metastatic classes of samples.</p> <p>Results</p> <p>We propose an alternative concept of analysis that considers the entire multidimensional space of gene expression and identifies the partition of this space in which metastatic development is possible. To apply this concept in cancer gene expression studies we utilize a modification of high-dimension natural taxonomy algorithm FOREL. Our analysis of microarray data containing primary and metastatic cancer samples has revealed not only differentially expressed genes, but also relations between different groups of primary and metastatic cancer. Metastatic samples tend to occupy a distinct partition of gene expression space. Further pathway analysis suggests that this partition is delineated by a specific pattern of gene expression in cytoskeleton remodeling, cell adhesion and apoptosis/cell survival pathways. We compare our findings with both report of original analysis and recent studies in molecular mechanism of metastasis.</p> <p>Conclusion</p> <p>Our analysis indicates a single molecular mechanism of metastasis. The new approach does not contradict previously reported findings, but reveals important details unattainable with traditional methodology.</p

Knowledge-based ontology concept for numerical data clustering

Classical clustering algorithms are sufficiently well studied, they are used for grouping numerical data in similar structures - clusters. Similar objects are placed in the same cluster, different objects in another cluster. All of the classic clustering algorithms have common parameters, and successful selection of which also determines the clustering result. The most important parameters characterizing clustering are: clus- tering algorithm, metrics, initial number of clusters, criteria for clustering accuracy. In recent years, there has been a tendency towards the possibility of obtaining rules from clusters. Classical clustering algorithms do not apply semantic knowledge. It creates difficulties in interpreting the results of clustering. Presently, the use of ontology opportunities is developing very rapidly, that allows to gain knowledge about a certain data model. The paper analyzes the concept of ontology and prototype development for numerical data clusterization, which includes the most significant indicators characterizing clusterization. The aim of the work is to develop a concept for analyzing clustering data with the help of ontologies. As a result of the work, a study has been conducted on the use of ontologies in this type of tasks

Концепция использования онтологий в системах анализа рисков

The work is dedicated to the development of an ontology concept for assessment risk of threats for information systems on the Microsoft approach – the model of identifying threats STRIDE and methodology DREAD for assessment risk of threats. The aim of the study is to describe the security implementation methodology of information systems offered by Microsoft. The basic concepts and techniques of this model are given and ontology concept of risk assessment is proposed, some of the classes and subclasses of the developed ontology are described.Работа посвящена разработке концепции онтологии для оценки рисков угроз информационным системам по подходу Microsoft - модели идентификации угроз STRIDE и методики DREAD для оценки рисков угроз. Даны основные понятия и методики этой модели и предложена концепция онтологии по оценке рисков, описаны некоторые классы и подклассы разрабатываемой онтологии

Концепция использования онтологий в системах анализа рисков

The work is dedicated to the development of an ontology concept for assessment risk of threats for information systems on the Microsoft approach – the model of identifying threats STRIDE and methodology DREAD for assessment risk of threats. The aim of the study is to describe the security implementation methodology of information systems offered by Microsoft. The basic concepts and techniques of this model are given and ontology concept of risk assessment is proposed, some of the classes and subclasses of the developed ontology are described.Работа посвящена разработке концепции онтологии для оценки рисков угроз информационным системам по подходу Microsoft - модели идентификации угроз STRIDE и методики DREAD для оценки рисков угроз. Даны основные понятия и методики этой модели и предложена концепция онтологии по оценке рисков, описаны некоторые классы и подклассы разрабатываемой онтологии

Dynamics of quasi-stationary systems: Finance as an example

We propose a combination of cluster analysis and stochastic process analysis to characterize high-dimensional complex dynamical systems by few dominating variables. As an example, stock market data are analyzed for which the dynamical stability as well as transitions between different stable states are found. This combined method also allows to set up new criteria for merging clusters to simplify the complexity of the system. The low-dimensional approach allows to recover the high-dimensional fixed points of the system by means of an optimization procedure.Comment: 6 page

Through entrepreneurs’ eyes: the Fab-spaces constellation

Fab-spaces provide individuals with access to numerous manufacturing equipment (including additive manufacturing), to carry out different types of projects. Although scholars are starting to speculate about the importance of these new organizational forms and their potential for future distributed innovation and production ecologies, this phenomenon is still largely unexplored. Building on existing multidisciplinary research, this paper offers the first empirical analysis of existing fab-spaces as providers of knowledge and production competencies. Amongst all the possible perspectives to derive a framework, we choose that of fab-spaces users who have an entrepreneurial intention. After deriving an analytical framework to position fab-spaces in the current academic discourse, the paper develops a classification, which considers the competences available to entrepreneurs, via fab-spaces, in conjunction with how these competences are provided. The resulting map reveals the complementarities amongst the different fab-spaces. It also shows that the current portfolio of fab-spaces supports mainly the distribution of innovation across locations and social groups. Several types of fab-spaces are currently well placed to support the transition from innovation to manufacturing, but their geographical distribution and range of manufacturing capabilities is not yet enough to provide a fully distributed manufacturing model. This study has practical consequences for entrepreneurs, in the better identification of the appropriate fab-spaces for their needs, and for policy makers, to help position the different types of fab-spaces as elements for national systems of innovation and production.Engineering and Physical Sciences Research Council (Grant ID: EP/K039598/1)This is the final version of the article. It first appeared from Taylor & Francis via http://dx.doi.org/10.1080/00207543.2016.119850