PSOClustering
MinSizeKmeans
| PSOClustering | MinSizeKmeans | |
|---|---|---|
| 1 | 1 | |
| 14 | 80 | |
| - | - | |
| 0.0 | 0.0 | |
| almost 2 years ago | about 3 years ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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PSOClustering
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Need help understanding Particle Swarm Optimization
I apologize for the very basic question, but I have been having trouble understanding this. So I get PSO in terms of finding a maximum or minimum, given a function. But where I am lost is applying the algorithm to data sets. The one I am looking at is applying the process to the Iris Flower Dataset.
MinSizeKmeans
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Preliminary Evidence that Retail Trades can be Identified and Counted on the Tape
Sure so basically I scraped the volume from the SEC report figure depicting 'Buy' volume by measuring pixels between ticks on the y axis. I also downloaded all regular session trades for the dates in that figure and after some rounds of data cleaning (ie one hot encoding trade condition data) I ran the trades from the first half through an implementation of kmeans clustering with minimum cluster size constraints set to cluster the trades weighted by volume into 2 groups with minimum weight of the volume scraped from the candle from the SEC report. This clustering takes a long time to run so I've only managed to process that first bar.
What are some alternatives?
kmodes - Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data
PSO-cont-sched - Made for a college project, this Java program attempts to demonstrate how PSO might be used to solve container scheduling problems.
Clustering4Ever - C4E, a JVM friendly library written in Scala for both local and distributed (Spark) Clustering.
scikit-opt - Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)
MAGIST-Algorithm - Multi-Agent Generally Intelligent Simultaneous Training Algorithm for Project Zeta
zoofs - zoofs is a python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It's easy to use , flexible and powerful tool to reduce your feature size.
fuzzy-c-means - A simple python implementation of Fuzzy C-means algorithm.
AnnA_Anki_neuronal_Appendix - Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity