KMeansCluster
Tool
Summary
Prior to using KMeansCluster, you will use the FeatureExtract
tool to extract features from the candidate fragments. Once
features are extracted, you’ll move to one of the
“cluster” tools, such as KMeansCluster, to group
related spikes. KMeansCluster uses KMeans (an algorithm
to cluster objects based on attributes into k partitions)
to assign each fragment to a specific cluster. Ideally,
each cluster of spikes can be determined to be from a unique
neuron’s firing
Screen Shot
Details'
Clustering is the classification of objects into different
groups, or more precisely, the partitioning of a data set
into subsets (clusters), so that the data in each subset (ideally)
share some common trait - often proximity according to some
defined distance measure.
The K-means algorithm assigns each point to the cluster whose
center (also called centroid) is nearest. The center is the
average of all the points in the cluster — that is,
its coordinates are the arithmetic mean for each dimension
separately over all the points in the cluster.
The algorithm steps are (J. MacQueen, 1967):
1. Choose the number of clusters, k.
2. Randomly generate k clusters and determine the cluster
centers, or directly generate k random points as cluster centers.
3. Assign each point to the nearest cluster center.
4. Recompute the new cluster centers.
5. Repeat the two previous steps until some convergence criterion
is met (usually that the assignment hasn't changed).
The main advantages of this algorithm are its simplicity
and speed which allows it to run on large datasets. Its disadvantage
is that it does not yield the same result with each run, since
the resulting clusters depend on the initial random assignments.
It minimizes intra-cluster variance, but does not ensure that
the result has a global minimum of variance.
1 From Wikipedia.org, Cluster Analysis
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