josh.meanings

A mean is a center of points. A means, a way of accomplishing that which is sought. Meaning, the latent concept vector that does not vary even though the words which express it might. This library though? The central idea behind it is to provide you a means of finding means so that you might come to know meanings through the process of repeatedly calculating means.

This is a program for computing k-means in Clojure. It is built to handle workloads which involve datasets which are too large to fit in memory, but not so large that the computation cannot be persisted to disk.

Installation

Getting Started

(require `[josh.meanings.kmeans :refer [k-means k-means-seq]
         `[josh.meanings.protocols.savable :refer [save-model]]
         `[josh.meanings.protocols.classifer :refer [classify load-centroids load-assignments]])


;; Get a dataset.  You can pass in your dataset under a variety of formats. 
;; See the docs for more details on supported formats.
(def dataset "your_dataset.csv")  

;; Choose the number of clusters you want
(def k 10)


;; To get a single cluster model
(def model (k-means dataset k))

;; Alternatively you can run k means multiple times.  This is recommended because 
;; some k means initializations don't give guarantees on the quality of a solution 
;; and so you can get better results by running k means multiple times and taking 
;; the best result.
(def model (apply min-key :cost (take k-tries (k-means-seq cluster-dataset-name k)))))

;; Once you have a model you can save it.
(def model-path (.save-model model))

;; Later you can load that model
(def model (load-model model-path))

;; To load the assignments just
(.load-assignments model)

;; To classify a new entry
(.classify model [1 2 3])

;; To view the centroids
(.load-centroids model)