BigDataFr recommends: Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data Subjects: Machine Learning (stat.ML) […] Clustering datasets with a mix of continuous and categorical attributes is encountered routinely by data analysts. This work presents a method to clustering such datasets using Homogeneity Analysis. An Optimal Euclidean representation of mixed datasets is obtained […]
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[arXiv] BigDataFr recommends: Asynchronous Parallel Algorithms for Nonconvex Big-Data Optimization
BigDataFr recommends: Asynchronous Parallel Algorithms for Nonconvex Big-Data Optimization: Model and Convergence Subjects: Optimization and Control (math.OC); Distributed, Parallel, and Cluster Computing (cs.DC) […] We propose a novel asynchronous parallel algorithmic framework for the minimization of the sum of a smooth nonconvex function and a convex nonsmooth regularizer, subject to both convex and nonconvex constraints. […]
[Datasciencecentral] BigDataFr recommends: A methodology for solving problems with DataScience for Internet of Things – Full – #iot
BigDataFr recommends: A methodology for solving problems with DataScience for Internet of Things – Part 1 and 2 […] This two part blog is based on my forthcoming book: Data Science for Internet of Things. It is also the basis for the course I teach Data Science for Internet of Things Course. I will be […]
[arXiv] BigDataFr recommends: Representation of functions on big data associated with directed graphs
BigDataFr recommends: Representation of functions on big data associated with directed graphs Subjects: Classical Analysis and ODEs (math.CA) […] This paper is an extension of the previous work of Chui, Filbir, and Mhaskar (Appl. Comput. Harm. Anal. 38 (3) 2015:489-509), not only from numeric data to include non-numeric data as in that paper, but also […]
[Datasciencecentral] BigDataFr recommends: Ableism in the Numbers – Social Metrification #datascientist
BigDataFr recommends: Ableism in the Numbers – Social Metrification […] Ableism (able + ism) is apparent in many interactions between people. While driving on a road having a posted limit of 60 KPH, I was traveling slower since I expected a red light to soon appear ahead of me. The driver behind me – at […]
[arXiv] BigDataFr recommends: Measuring Economic Activities of China with Mobile Big Data
BigDataFr recommends: Measuring Economic Activities of China with Mobile Big Data Subjects: Social and Information Networks (cs.SI); Computers and Society (cs.CY) […] Emerging trends in smartphones, online maps, social media, and the resulting geo-located data, provide opportunities to collect traces of people’s socio-economical activities in a much more granular and direct fashion, triggering a revolution […]
[Datasciencecentral] BigDataFr recommends: Applications of Deep Learning
BigDataFr recommends: Applications of Deep Learning […] This post highlights a number of important applications found for deep learning so far. It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from. It can include images of accidents, text notes of […]
[arXiv] BigDataFr recommends: Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis
BigDataFr recommends: Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis in the context of RADICAL city applications Subjects: Computers and Society (cs.CY); Learning (cs.LG); Social and Information Networks (cs.SI) […] In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and analysis of big […]
[Datasciencecentral – Top] BigDataFr recommends: 40 Techniques Used by Data Scientists
BigDataFr recommends: 40 Techniques Used by Data Scientists […] These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 40 links below, you will find a selection […]
[arXiv] BigDataFr recommends: Limited Random Walk Algorithm for Big Graph Data Clustering
BigDataFr recommends: Limited Random Walk Algorithm for Big Graph Data Clustering Subjects: Social and Information Networks (cs.SI); Physics and Society (physics.soc-ph) […]Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method restricts the reach […]