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3.3 Initialization of K-Means Clustering

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Анализ кластеров в процессе анализа данных

Иллинойсский университет в Урбане-Шампейне

4.4 (оценок: 224) | Зарегистрировано учащихся: 22K

Курс 5 из 6 — Интеллектуальный анализ данных Специализация

Просмотреть программу курса

Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.

Получаемые навыки

Cluster Analysis, Data Clustering Algorithms, K-Means Clustering, Hierarchical Clustering

Рецензии

4.4 (оценок: 224)

5 stars
143 ratings
4 stars
54 ratings
3 stars
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1 star
7 ratings

DD

Sep 25, 2017

A very good course, it gives me a general idea of how clustering algorithm work.

TK

Oct 10, 2017

Very intense and required complex thinking and programming skill

Из урока

Week 2

3.1 Partitioning-Based Clustering Methods3:29

3.2 K-Means Clustering Method9:22

3.3 Initialization of K-Means Clustering4:38

3.4 The K-Medoids Clustering Method6:59

3.5 The K-Medians and K-Modes Clustering Methods6:24

3.6 Kernel K-Means Clustering8:12

Преподаватели

Jiawei Han
Abel Bliss Professor

[SOUND] As we just had discussed,

the quality of K-means clustering is quite sensitive to the initial relation status.

Then we need to see how to do good in initialization so

we find a good quality clustering using K-means method.

I just gave you a simple example everybody can see.

Different initializations may generate rather different clustering results.

Some may not be optimal at all.

Just give you example, suppose we have only four points,

we will find two clusters.

If we initialize in the solid circle.

That means these two points get into one cluster like if we give you the center

like this two, they will attract these two and these two as in one cluster.

Then actually it becomes stabilized, because you calculate the center,

the center is still here.

They will not move.

So then you find a pretty ugly cluster, because you perceive these,

if you vertically group things into two clusters.

The sum of the square distance or SSE actually become rather small.

From this point of view we know the quality of

a clustering could be very sensitive to the initialization.

More recently, MacQueen in 1967 said we should select the k seeds randomly.

Now we need to run this algorithm multiple times using different seeds.

In some software package, they may even say,

run these algorithms 200 times.

Finally find which one you derive the best K-Means cluster simply

says you want to find the SSE, the sum of the square arrow is minimized.

There are many other methods also proposed for better initialization.

For example, there's one called K-Means++, proposed in 2007.

Essentially this ++ initialization is as follows.

The first centroid that you can select randomly, but then the next centroid to be

selected, you try to find the farthest point from the currently selected points.

That means a selection based on weighted probability score

based on a different probability you select which is best for the next one.

Then this selection continues until all the k centroids are obtained.

That initialization is done.

Then we can run the K-Means algorithm.

I'll give you a simple example of protein C,

poor initialization may lead to some poor clustering.

For example we take the same data sets,

those black points as we've shown before, now we give you two

same choice initialized as the red lines here, the blue lines here.

With this initialization, we can get clusters,

we can assign these objects into two different clusters in different colors.

One, the upper part, those parts assigned to the red cluster.

The lower part assigned to the blue cluster.

Then we can recalculate the center again.

You can probably see the center actually moved.

And with this recalculation we can re-assign those points.

You can run this if you want.

You probably can see finally they're stable like this, but actually,

this is not a very good cluster at all.

That means that this run of the K-Means generates a poor quality clustering.

That is simply giving us a simple show.

We do need some smart interrelations or

multiple random initialization is the best ones.

So this is the initialization of K-Means clustering.

[MUSIC]

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