<commit_before># User Manual

## Introduction

AMVIDC is a data clustering algorithm based on agglomerative 
hierarchical clustering (AHC) which uses minimum volume increase (MVI) 
and minimum direction change (MDC) as clustering criteria. The
algorithm is presented in detail in the following publication:

-   <NAME>., <NAME>., <NAME>., <NAME>., <NAME>., Spectrometric
differentiation of yeast strains using minimum volume increase and minimum direction change clustering criteria,
Pattern Recognition Letters (2014), doi: http://dx.doi.org/10.1016/j.patrec.2014.03.008
(available at http://www.sciencedirect.com/science/article/pii/S0167865514000889)

### Data format

Data for clustering is presented as a set of samples (or points), each 
with a constant number of dimensions. As such, for the rest of this 
guide, data matrices are considered to be in the following format:

-   *m* x *n*, with *m* samples (points) and *n* dimensions (variables)

### Generating data

AMVIDC was inspired on the differentiation of spectrometric data. 
However, to further validate the clustering algorithms, synthetic
data sets can be generated with the [generateData](generateData.m) 
function. This function generates data in the *m* x *n* format, with 
*m* samples (points) and *n* dimensions (variables) according to a set 
of parameters, which are explained in the source code.

## Running the algorithm<commit_msg>Documentation: add article link to article name<commit_after># User Manual

## Introduction

AMVIDC is a data clustering algorithm based on agglomerative 
hierarchical clustering (AHC) which uses minimum volume increase (MVI) 
and minimum direction change (MDC) as clustering criteria. The
algorithm is presented in detail in the following publication:

-   <NAME>., <NAME>., <NAME>., <NAME>., Rosa, 
A.C., [Spectrometric differentiation of yeast strains using minimum volume 
increase and minimum direction change clustering criteria](http://www.sciencedirect.com/science/article/pii/S0167865514000889),
Pattern Recognition Letters (2014), doi: http://dx.doi.org/10.1016/j.patrec.2014.03.008

### Data format

Data for clustering is presented as a set of samples (or points), each 
with a constant number of dimensions. As such, for the rest of this 
guide, data matrices are considered to be in the following format:

-   *m* x *n*, with *m* samples (points) and *n* dimensions (variables)

### Generating data

AMVIDC was inspired on the differentiation of spectrometric data. 
However, to further validate the clustering algorithms, synthetic
data sets can be generated with the [generateData](generateData.m) 
function. This function generates data in the *m* x *n* format, with 
*m* samples (points) and *n* dimensions (variables) according to a set 
of parameters, which are explained in the source code.

## Running the algorithm
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