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A Distance Scaling Method to Improve Density-Based Clustering
These functions implement a distance scaling method, proposed by Ye Zhu, Kai Ming Ting, and Maia Angelova, "A Distance Scaling Method to Improve Density-Based Clustering", in PAKDD2018 proceedings: https://doi.org/10.1007/978-3-319-93040-4_31.
This project aims to develop and share fast frequent subgraph mining and graph learning algorithms. Currently we release the frequent subgraph mining package FFSM and later we will include new functions for graph regression and classification package
...This packages contains the necessary codes to run on MATLAB.
2. The second package includes source and object files of DEMass-DBSCAN to be used with the WEKA system.
3. The third package DEMassBayes includes the source and object files of a Bayesian classifier using DEMass. DEMassBayes.7z has jar file to be used with WEKA and a readme file listing parameters used. The source files are included in DEMassBayes_Source.7z.
4. The four package is MassTER includes source and JAR file to be used with WEKA system..
CDTB is a MATLAB toolbox which performs Community Detection
...The CDTB contains several functions from the following categories.
1. graph generators;
2. clustering algorithms;
2. cluster number selection functions;
4. clustering evaluation functions.
Furthermore, CDTB is designed in a parametric manner so that the user can add his own functions and extensions.
The CDTB can be used in at least three ways. The user can employ the functions from the MATLAB command line; or he can write his own code, incorporating the CDTB functions; or he can use the Graphical User Interface (GUI) which automates the community detection and includes some data visualization options.
This project applies an interpretation of a k-NN algorithm to a library of GPS commuter data for speed prediction. The overall goal is to lay the foundation for a power management protocol for use in electric vehicles with hybrid energy storage.
A graphical MatLab framework for estimating the parameters of, modeling and simulating static and dynamic linear and polynomial systems in the errors-in-variables context with the intent of comparing various estimation strategies.