Clustering is a powerful machine learning method for discovering similar patterns according to the proximity of elements in feature space. It is widely used in computer science, bioscience, geoscience, and economics. Although the state-of-the-art partition-based and connectivity-based clustering methods have been developed, weak connectivity and heterogeneous density in data impede their effectiveness. In this work, we propose a boundary-seeking Clustering algorithm using the local Direction Centrality (CDC). It adopts a density-independent metric based on the distribution of K-nearest neighbors (KNNs) to distinguish between internal and boundary points. The boundary points generate enclosed cages to bind the connections of internal points, thereby preventing cross-cluster connections and separating weakly-connected clusters. We demonstrate the validity of CDC by detecting complex structured clusters in challenging synthetic datasets, identifying cell types from single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF) data, recognizing speakers on voice corpuses, and testifying on various types of real-world benchmarks.

Geospatial resource discovery is a critical step for developing geographic science applications. With the increasing number of geospatial resources available online, many Spatial Data Infrastructure (SDI) components (e.g. catalogues and portals) have been developed to help manage and discover geospatial resources. However, efficient and accurate geospatial resource discovery is still a big challenge because of the heterogeneity and complexity of decentralized network environments and interdisciplinary semantics. In this article, we report a search engine framework for efficient geospatial resource discovery, which reduces integration costs by leveraging existing Geospatial Cyberinfrastructure (GCI) components. Specifically, (1) the framework provides integration capability and flexibility by adopting the brokering approach, implementing a ‘plug-in’-based framework for metadata processing and proposing a dynamically configurable search workflow; (2) the asynchronous messaging and batch processing-based metadata record retrieval mode enhances the search performance and user interactivity; (3) an embedded semantic support system improves the discovery recall level and precision by providing semantic-based search rule creation and result similarity evaluation functions and (4) the engine assists user decision-making by integrating a service quality monitoring and evaluation system, data/service visualization tools, multiple views and additional information. Experiments and a search example show that the proposed engine helps both scientists and general users search for more accurate results with enhanced performance and user experience through a user-friendly interface.

Cloud computing is becoming the new generation computing infrastructure, and many cloud vendors provide different types of cloud services. How to choose the best cloud services for specific applications is very challenging. Addressing this challenge requires balancing multiple factors, such as business demands, technologies, policies and preferences in addition to the computing requirements. This paper recommends a mechanism for selecting the best public cloud service at the levels of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). A systematic framework and associated workflow include cloud service filtration, solution generation, evaluation, and selection of public cloud services. Specifically, we propose the following: a hierarchical information model for integrating heterogeneous cloud information from different providers and a corresponding cloud information collecting mechanism; a cloud service classification model for categorizing and filtering cloud services and an application requirement schema for providing rules for creating application-specific configuration solutions; and a preference-aware solution evaluation mode for evaluating and recommending solutions according to the preferences of application providers. To test the proposed framework and methodologies, a cloud service advisory tool prototype was developed after which relevant experiments were conducted. The results show that the proposed system collects/updates/records the cloud information from multiple mainstream public cloud services in real-time, generates feasible cloud configuration solutions according to user specifications and acceptable cost predication, assesses solutions from multiple aspects (e.g., computing capability, potential cost and Service Level Agreement, SLA) and offers rational recommendations based on user preferences and practical cloud provisioning; and visually presents and compares solutions through an interactive web Graphical User Interface (GUI).