Aim/Purpose: The clustering techniques are normally considered to determine the significant and meaningful subclasses purposed in datasets. It is an unsupervised type of Machine Learning (ML) where the objective is to form groups from objects based on their similarity and used to determine the implicit relationships between the different features of the data. Cluster Analysis is considered a significant problem area in data exploration when dealing with arbitrary shape problems in different datasets. Clustering on large data sets has the following challenges: (1) clusters with arbitrary shapes; (2) less knowledge discovery process to decide the possible input features; (3) scalability for large data sizes. Density-based clustering has been known as a dominant method for determining the arbitrary-shape clusters. Background: Existing density-based clustering methods commonly cited in the literature have been examined in terms of their behavior with data sets that contain nested clusters of varying density. The existing methods are not enough or ideal for such data sets, because they typically partition the data into clusters that cannot be nested. Methodology: A density-based approach on traditional center-based clustering is introduced that assigns a weight to each cluster. The weights are then utilized in calculating the distances from data vectors to centroids by multiplying the distance by the centroid weight. Contribution: In this paper, we have examined different density-based clustering methods for data sets with nested clusters of varying density. Two such data sets were used to evaluate some of the commonly cited algorithms found in the literature. Nested clusters were found to be challenging for the existing algorithms. In utmost cases, the targeted algorithms either did not detect the largest clusters or simply divided large clusters into non-overlapping regions. But, it may be possible to detect all clusters by doing multiple runs of the algorithm with different inputs and then combining the results. This work considered three challenges of clustering methods. Findings: As a result, a center with a low weight will attract objects from further away than a centroid with higher weight. This allows dense clusters inside larger clusters to be recognized. The methods are tested experimentally using the K-means, DBSCAN, TURN*, and IDCUP algorithms. The experimental results with different data sets showed that IDCUP is more robust and produces better clusters than DBSCAN, TURN*, and K-means. Finally, we compare K-means, DBSCAN, TURN*, and to deal with arbitrary shapes problems at different datasets. IDCUP shows better scalability compared to TURN*. Future Research: As future recommendations of this research, we are concerned with the exploration of further available challenges of the knowledge discovery process in clustering along with complex data sets with more time. A hybrid approach based on density-based and model-based clustering algorithms needs to compare to achieve maximum performance accuracy and avoid the arbitrary shapes related problems including optimization. It is anticipated that the comparable kind of the future suggested process will attain improved performance with analogous precision in identification of clustering shapes.

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Are you highly inquisitive with a committed approach to improving performance? Do you want to make an impact on the future of Smartphone and Laptop computing?
We are looking for experienced engineers with a strong understanding of computer architecture and performance analysis to investigate emerging use-cases such as AR and ML to help define future IP from Arm and our partners.

About the role

As a senior member of the engineering team within the Client Line of Business you will lead performance analysis investigations, producing data-led analysis and conclusions which help define requirements for future Client compute solutions.
Client computing devices are expected to deliver incredible performance across an increasing range of diverse use cases including AAA quality gaming, compelling AR experiences and applications with deeply embedded AI and ML.
You will use your knowledge of hardware and software to build a deep understanding of critical use cases. You will look at how workloads utilise available compute and memory resources, how advancements in SoC topologies, processor design and software will help improve user experience.

We are looking for an enthusiastic software developer with understanding of Java or modern C++, to join the Arm Mobile Studio team.

The role involves collaborating with highly motivated developers from different backgrounds, and customers throughout the world, to craft the next generation of our performance analysis tools for Arm CPUs and Mali GPUs. As part of this team, you would help create new features, maintain existing ones, and support the engineering infrastructure for build, test, and continuous integration. We also help to support both internal and external customers, and contribute to our developer documentation, developer website, and community forums.

We are growing the team to help deliver features that support the full breadth of Arm's product portfolio. Our tools are used to optimize the latest smart cars, drones, mobile games, and machine learning applications, your ideas will make a difference and help to bring world-beating products to market.

Over the past decade, ptychography has been proven to be a robust tool for non-destructive high-resolution quantitative electron, X-ray and optical microscopy. It allows for quantitative reconstruction of the specimen's transmissivity, as well as recovery of the illuminating wavefront. Additionally, various algorithms have been developed to account for systematic errors and improved convergence. With fast ptychographic microscopes and more advanced algorithms, both the complexity of the reconstruction task and the data volume increase significantly. PtychoShelves is a software package which combines high-level modularity for easy and fast changes to the data-processing pipeline, and high-performance computing on CPUs and GPUs.

View definitions are created for deterministic performance analysis in real-time computing systems, and can then be used to present views for analyzing outliers that occur during run-time execution. Trace data created by a real-time application is compared to a set of view definitions to determine whether the trace data matches the view definition. If so, then related records from the trace are gathered according to specifications in the matched view definition, and calculations (such as elapsed time) can then be performed using the related records. A view definition may be created by prompting a user for selection of parameters to be programmatically inserted into a markup language document. A capability may be provided whereby a user can receive additional information (which is extracted from the trace data, according to specifications in the matched view definition) upon a user gesture such as hovering a selection means over a displayed view.

Cham : Palgrave Macmillan, 2020.

[S.l.] : PALGRAVE MACMILLAN, 2020.

Online Resource

IEEE / Institute of Electrical and Electronics Engineers Incorporated

IEEE Computer Society