More on Hierarchical Clustering

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Machine Learning with Python

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IBM

Machine Learning with Python

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Cours 8 sur 9 dans Specialization IBM Data Science Professional Certificate

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies in the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

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Clustering

In this section, you will learn about different clustering approaches. You learn how to use clustering for customer segmentation, grouping same vehicles, and also clustering of weather stations. You understand 3 main types of clustering, including Partitioned-based Clustering, Hierarchical Clustering, and Density-based Clustering.

Intro to Hierarchical Clustering6:18

More on Hierarchical Clustering5:51

Rencontrer les enseignants

SAEED AGHABOZORGI
Ph.D., Sr. Data Scientist
IBM Developer Skills Network

Hello and welcome.

In this video, we'll be covering more details about hierarchical clustering.

Let's get started.

Let's look at agglomerative algorithm for hierarchical clustering.

Remember that agglomerative clustering is a bottom up approach.

Let's say our data set has n data points.

First, we want to create n clusters, one for each data point.

Then, each point is assigned as a cluster.

Next, we want to compute the distance proximity matrix

which will be an n by n table.

After that, we want to iteratively run the following steps until the specified

cluster number is reached, or until there is only one cluster left.

First, merge the two nearest clusters.

Distances are computed already in the proximity matrix.

Second, update the proximity matrix with the new values.

We stop after we've reached the specified number of clusters, or

there is only one cluster remaining with the result stored in a dendogram.

So in the proximity matrix, we have to measure the distances between clusters and

also merge the clusters that are nearest.

So, the key operation is the computation of the proximity between

the clusters with one point and also clusters with multiple data points.

At this point, there are a number of key questions that need to be answered.

For instance, how do we measure the distances between these clusters and

how do we define the nearest among clusters?

We also can ask, which points do we use?

First, let's see how to calculate the distance between two clusters

with one point each.

Let's assume that we have a data set of patients and

we want to cluster them using hierarchy clustering.

So our data points are patients with a featured set of three dimensions.

For example, age, body mass index, or BMI and blood pressure.

We can use different distance measurements to calculate the proximity matrix.

For instance, Euclidean distance.

So, if we have a data set of n patience,

we can billed an n by n dissimilarity distance matrix.

It will give us the distance of clusters with one data point.

However, as mentioned, we merge clusters in agglomerative clustering.

Now the question is, how can we calculate the distance between clusters

when there are multiple patients in each cluster?

We can use different criteria to find the closest clusters and merge them.

In general, it completely depends on the data type, dimensionality of data and

most importantly, the domain knowledge of the data set.

In fact, different approaches to defining the distance between clusters

distinguish the different algorithms.

As you might imagine, there are multiple ways we can do this.

The first one is called single linkage clustering.

Single linkage is defined as the shortest distance between two points in

each cluster, such as point a and b.

Next up is complete linkage clustering.

This time we are finding the longest distance between the points in

each cluster, such as the distance between point a and b.

The third type of linkage is average linkage clustering or the mean distance.

This means we're looking at the average distance of each point from one cluster

to every point in another cluster.

The final linkage type to be reviewed is centroid linkage clustering.

Centroid is the average of the feature sets of points in a cluster.

This linkage takes into account the centroid of each cluster

when determining the minimum distance.

There are three main advantages to using hierarchical clustering.

First, we do not need to specify the number of clusters required for

the algorithm.

Second, hierarchical clustering is easy to implement.

And third, the dendrogram produced is very useful in understanding the data.

There are some disadvantages as well.

First, the algorithm can never undo any previous steps.

So for example, the algorithm clusters two points and

later on, we see that the connection was not a good one.

The program can not undo that step.

Second, the time complexity for the clustering can result in very long

computation times in comparison with efficient algorithms such as K-means.

Finally, if we have a large data set, it can become difficult to determine

the correct number of clusters by the dendrogram.

Now, lets compare hierarchical clustering with K-means.

K-means is more efficient for large data sets.

In contrast to K-means,

hierarchical clustering does not require the number of cluster to be specified.

Hierarchical clustering gives more than one partitioning depending on

the resolution or as K-means gives only one partitioning of the data.

Hierarchical clustering always generates the same clusters,

in contrast with K-means, that returns different clusters each time it is run,

due to random initialization of centroids.

Thanks for watching.

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