unsupervised machine learning looks for
patterns and datasets that don't have
labeled responses you'd use this
technique when you want to explore your
data but don't yet have a specific goal
or you're not sure what information the
data contains it's also a good way to
reduce the dimension of your data as
we've previously discussed most
unsupervised learning techniques are a
form of cluster analysis which separates
data into groups based on shared
characteristics clustering algorithms
fall into two broad groups hard
clustering where each data point belongs
to only one cluster and soft clustering
where each data point can belong to more
than one cluster for context here's a
hard clustering example say you're an
engineer building cell phone towers you
need to decide where and how many towers
to construct to make sure you're
providing the best signal reception you
need to locate the towers within
clusters of people to start you need an
initial guess at the number of clusters
to do this compare scenarios with three
towers and four towers to see how well
each is able to provide service because
a phone can only talk to one tower at a
time this is a hard clustering problem
for this you could use k-means
clustering because the k-means algorithm
treats each observation in the data as
an object having a location in space it
finds cluster centers or means that
reduce the total distance from data
points to their cluster centers so that
was hard clustering let's see how you
might use a soft clustering algorithm in
the real world
pretend you're a biologist analyzing the
genes involved in normal and abnormal
cell division you have data from two
tissue samples and you want to compare
them to determine whether certain
patterns of gene features correlate to
cancer because the same genes can be
involved in several biological processes
no single gene is likely to belong to
one cluster only apply a fuzzy C means
algorithm to the data and then visualize
the clusters to see which groups of
genes behave in similar ways you can
then use this model to help see which
features correlate with normal or
abnormal cell division this covers the
two main techniques hard
soft clustering for exploring data with
unlabeled responses remember though that
you can also use unsupervised machine
learning to reduce the number of
features or the dimensionality of your
data you do this to make your data less
complex especially if you're working
with data that has hundreds or thousands
of variables by reducing the complexity
of your data you're able to focus on the
important features and gain better
insights let's look at three common
dimensionality reduction algorithms
principal component analysis or PCA
performs a linear transformation on the
data so that most of the variance in
your data set is captured by the first
few principal components this could be
useful for developing condition
indicators for machine health monitoring
factor analysis identifies underlying
correlations between variables in your
data set it provides a representation of
unobserved latent or common factors
factor analysis is sometimes used to
explain stock price variation
non-negative matrix factorization is
used when model terms must represent
non-negative quantities such as physical
quantities if you need to compare a lot
of text on webpages or documents this
would be a good method to start with as
text is either not present or occurs a
positive number of times in this video
we took a closer look at hard and soft
clustering algorithms and we also showed
why you'd want to use unsupervised
machine learning to reduce the number of
features in your data set as for your
next steps unsupervised learning might
be your end goal if you're just looking
to segment data a clustering algorithm
is an appropriate choice on the other
hand you might want to use unsupervised
learning as a dimensionality reduction
step for supervised learning in our next
video we'll take a closer look at
supervised learning for now that wraps
up this video don't forget to check out
the description below for more resources
and links