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Erschienen in:
Statistics, Statisticians, and the Internet of Things Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

16. Q3-D3-LSA: D3.js and Generalized Vector Space Models for Statistical Computing

verfasst von : Lukas Borke, Wolfgang K. Härdle

Erschienen in: Handbook of Big Data Analytics

Verlag: Springer International Publishing

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Abstract

QuantNet is an integrated web-based environment consisting of different types of statistics-related documents and program codes. Its goal is creating reproducibility and offering a platform for sharing validated knowledge native to the social web. To increase the information retrieval (IR) efficiency there is a need for incorporating semantic information. Three text mining models will be examined: vector space model (VSM), generalized VSM (GVSM), and latent semantic analysis (LSA). The LSA has been successfully used for IR purposes as a technique for capturing semantic relations between terms and inserting them into the similarity measure between documents. Our results show that different model configurations allow adapted similarity-based document clustering and knowledge discovery. In particular, different LSA configurations together with hierarchical clustering reveal good results under M 3 evaluation. QuantNet and the corresponding Data-Driven Documents (D3) based visualization can be found and applied under http://​quantlet.​de. The driving technology behind it is Q3-D3-LSA, which is the combination of “GitHub API based QuantNet Mining infrastructure in R”, LSA and D3 implementation.

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Vorheriges Kapitel Bridging Density Functional Theory and Big Data Analytics with Applications
Nächstes Kapitel A Tutorial on : R Package for the Linearized Bregman Algorithm in High-Dimensional Statistics
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Metadaten
Titel
Q3-D3-LSA: D3.js and Generalized Vector Space Models for Statistical Computing
verfasst von
Lukas Borke
Wolfgang K. Härdle
Copyright-Jahr
2018
Buch
Handbook of Big Data Analytics

Print ISBN: 978-3-319-18283-4

Electronic ISBN: 978-3-319-18284-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-18284-1_16

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