An R-tool for comprehensive science mapping analysis. A package for quantitative research in scientometrics and bibliometrics.
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Updated
Jun 9, 2025 - R
An R-tool for comprehensive science mapping analysis. A package for quantitative research in scientometrics and bibliometrics.
Inference of microbial interaction networks from large-scale heterogeneous abundance data
Deep Co-occurrence Feature Learning for Visual Object Recognition (CVPR 2017)
Co-occurrence Based Texture Synthesis
Code and data for extracting co-occurrence networks from Shakespeare's plays
A fast implementation of GloVe, with optional retrofitting
Tool for extracting topics, keywords and their collocates from a Dutch corpus. Includes and extends the functionality of the Keyword Generator.
Text Processing Using Hadoop
Texture Segmentation using: Gray-Level Co-occurence Matrix, Leung-Malik (LM) Filter Bank and Schmid (S) Filter Bank and Local Binary Pattern.
Unlabeled directed graph mining project from Co-occurrence graph of Document using gSpan algorithm based on Apache Spark
Co Occurrence Filter Matlab implementation.
Work in Fintech-Text-Mining-and-Machine-Learning class
R package for analyzing microbial co-occurences
Data Analytics pipeline using Apache Spark | Build multi-class classification models | Test the model using test data and compute accuracy of each method
Movie Recommender System based on co-occurrence matrix/similarity matrix.
Compared the agenda setting strategies on the "Ractopamine Pork" Vote, one of the four questions in the 2021 Taiwanese Referendum, between pan-blue and pan-green media by using text mining approaches such as bag-of-words, w2v, topic model. Raw data were collected from four Taiwanese media (Chinatimes/TVBS/LTN/FTV) with Python Package BeautifulSoup.
NLP Project 2 - Using ount Vector, TF-IDF Vector, Co-occurrence Matrix for Frequency based embeddings and made Word2Vec model using Continuous Bag of Words (CBOW) and Skip-Gram (SG) for Prediction based Embeddings
It is important for a granting agency to know how the distribution of the applications qua disciplines is. ˆ How many applications belong to Exact Science disciplines, how many fall within one discipline? ˆ How many applications with disciplines outside exact sciences domain have been submitted?
This repository contains the data used for and generated during our research for the article "Neo-Assyrian Imperial Religion Counts: A Quantitative Approach to the Affiliations of Kings and Queens with Their Gods and Goddesses." The creation of this data has been funded by the Academy of Finland (decision numbers 298647, 312051, and 330727).
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