MinSizeKmeans
Clustering4Ever
| MinSizeKmeans | Clustering4Ever | |
|---|---|---|
| 1 | - | |
| 80 | 128 | |
| - | 0.8% | |
| 0.0 | 0.0 | |
| about 3 years ago | over 3 years ago | |
| Python | Scala | |
| GNU General Public License v3.0 only | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
MinSizeKmeans
-
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.
Clustering4Ever
We haven't tracked posts mentioning Clustering4Ever yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
kmodes - Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data
Smile - Statistical Machine Intelligence & Learning Engine
MAGIST-Algorithm - Multi-Agent Generally Intelligent Simultaneous Training Algorithm for Project Zeta
Breeze - Breeze is a numerical processing library for Scala.
PSOClustering - This is an implementation of clustering IRIS dataset with particle swarm optimization(PSO)
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
OpenMOLE - Workflow engine for exploration of simulation models using high throughput computing
feathr - Feathr – A scalable, unified data and AI engineering platform for enterprise
PredictionIO - PredictionIO, a machine learning server for developers and ML engineers.
Spark Notebook - Interactive and Reactive Data Science using Scala and Spark.
Compute.scala - Scientific computing with N-dimensional arrays
Spire - Powerful new number types and numeric abstractions for Scala.