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An Iterative Model for Text Mining Using Big Data Technology

https://doi.org/10.2139/ssrn.3356552
0 signed · 12 inferred · view as graph

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s10766-018-0591-9openalex-extracted
Charismatic Document Clustering Through Novel K-Means Non-negative Matrix Factorization (KNMF) Algorithm Using Key Phrase Extraction
inferredcites →https://doi.org/10.1016/j.fcij.2018.03.003openalex-extracted
An analysis of MapReduce efficiency in document clustering using parallel K-means algorithm
inferredcites →https://doi.org/10.1016/j.fcij.2018.06.002openalex-extracted
Partition based clustering of large datasets using MapReduce framework: An analysis of recent themes and directions
inferredcites →https://doi.org/10.1016/s1007-0214(05)70069-9openalex-extracted
Improvement and parallelism of k-means clustering algorithm
inferredcites →https://doi.org/10.1109/3pgcic.2014.136openalex-extracted
Challenges and Trends of Big Data Analytics
inferredcites →https://doi.org/10.1109/iadcc.2014.6779407openalex-extracted
Hadoop: Addressing challenges of Big Data
inferredcites →https://doi.org/10.1109/icecct.2017.8117888openalex-extracted
Parallel support vector machine used in map-reduce for risk analysis
inferredcites →https://doi.org/10.1109/icicict1.2017.8342543openalex-extracted
An evaluation of MapReduce framework in cluster analysis
inferredcites →https://doi.org/10.5120/12787-0024openalex-extracted
Document Clustering: A Review
inferredcites →https://doi.org/10.5120/ijais12-450691openalex-extracted
Document Clustering: A Detailed Review

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-981-99-1328-2_23openalex-snapshot
A Decision Feedback Model for Big Data Analytics in Smart Grid
inferred← citeshttps://doi.org/10.22364/bjmc.2024.12.1.04openalex-snapshot
Development of a New Conceptual Framework for Better Understanding of the Food Consumer: An Interdisciplinary Big Data Approach
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.