The document discusses K-means clustering and DBSCAN, two popular clustering algorithms. K-means clusters data by minimizing distances between points and cluster centroids. It works by iteratively assigning points to the closest centroid and recalculating centroids. DBSCAN clusters based on density rather than distance; it identifies dense regions separated by sparse regions to form clusters without specifying the number of clusters.

1. K-MEAN CLUSTER
BY CHENG ZHAN
HOUSTON MACHINE LEARNING MEETUP
1/7/2017

5. INTRODUCTION
• K-means (MacQueen, 1967) is one of the simplest
unsupervised learning algorithms that solve the well known
clustering problem.
• The main idea is to define k centroids, one for each
cluster.

6. • Input
• M(set of points)
• k(number of clusters)
• Output
• μ_1 , …, μ_k (cluster centroids)
• k-Means clusters the M point into K clusters by minimizing the
squared error function
μ

7. HOW TO PICK K

8. K-MEAN ALGORITHM
• 0. Initialize cluster centers
• 1. Assign observations to closest
cluster center
• 2. Revise cluster centers as mean of
assigned observations
• 3. Repeat 1&2 until convergence

9. K-MEAN ALGORITHM
• 0. Initialize cluster centers
• 1. Assign observations to closest
cluster center
• 2. Revise cluster centers as mean of
assigned observations
• 3. Repeat 1&2 until convergence

10. K-MEAN ALGORITHM
• 0. Initialize cluster centers
• 1. Assign observations to closest
cluster center
• 2. Revise cluster centers as mean of
assigned observations
• 3. Repeat 1&2 until convergence

11. K-MEAN ALGORITHM
• 0. Initialize cluster centers
• 1. Assign observations to closest
cluster center
• 2. Revise cluster centers as mean of
assigned observations
• 3. Repeat 1&2 until convergence

16. GOOD INITIAL POINTS

17. UNLUCKY

18. K-MEANS IN PRACTICE
• How to choose initial centroids
• select randomly among the data points
• generate completely randomly
• How to choose k
• study the data
• run k-Means for different k (measure squared error for each k)
• Run k-means many times!
• Get many choices of initial points

19. WHAT ABOUT THIS?

20. QUESTIONS
• Euclidean distance results in spherical clusters
• What cluster shape does the Manhattan distance give?
• Think of other distance measures. What cluster shapes
will those yield?

21. DBSCAN

22. DENSITY-BASED SPATIAL CLUSTERING OF APPLICATION
WITH NOISE
• DBSCAN is a Density-Based Clustering algorithm
• In density based clustering we partition points into dense regions separated
by not-so-dense regions.
• Important Questions:
• How do we measure density and what is a dense region?
• DBSCAN:
• Density at point p: number of points within a circle of radius Eps
• Dense Region: A circle of radius Eps that contains at least MinPts points

23. WHEN DBSCAN WORKS WELL

24. WHEN DBSCAN WORKS WELL

25. DBSCAN

26. DEFINITIONS

27. REACHABILITY AND CONNECTIVITY

28. DBSCAN ALGORITHM: EXAMPLE

29. DBSCAN ALGORITHM: EXAMPLE

30. DBSCAN ALGORITHM: EXAMPLE

31. DETERMINING EPS & MINPTS
• Idea is that for points in a cluster, their kth nearest neighbors
are at roughly the same distance
• Noise points have the kth nearest neighbor at farther distance
• So, plot sorted distance of every point to its kth nearest
neighbor
• Find the distance d where there is a “knee” in the curve
• Eps = d, MinPts = k

33. SENSITIVE TO PARAMETERS

34. SENSITIVE TO PARAMETERS

35. APPLICATIONS

36. DISTANCE METRIC FOR DOCUMENTS
• Motivations
• Identical – easy
• Modified or related (Ex: DNA, Plagiarism, Authorship)
• Did Francis Bacon write Shakespeare’s plays

37. DOCUMENT RETRIEVAL

38. CHALLENGES
• How do we measure similarity
• How do we search over articles

39. DOCUMENT REPRESENTATION
• Word count document representation
• Bag of words model
• Ignore order of words
• Count # of instances of each word in vocabulary

40. EXAMPLE
• Word: Sequence of alphanumeric characters. For example, the phrase “6.006
is fun” has 4 words.
• Word Frequencies: Word frequency D(w) of a given word w is the number of
times it occurs in a document D.
• For example, the words and word frequencies for the above phrase are as
below: Word 6 The Is 006 Easy Fun
Count 1 0 1 1 0 1

41. METRIC
• d(x,x) = 0
• d(x,y) = d(y,x)
• d(x,y) + d(y,z) >= d(x,z)

42. METRIC
• Inner product of the vectors D1 andD2 containing the word frequencies
for all words in the 2 documents. Equivalently, this is the projection of
vectors D1 onto D2 or vice versa. Mathematically this is expressed as:
D1 ·D2 = ∑ D1(w) .D2(w)
• Angle Metric: The angle between the vectors D1 and D2 gives an
indication of overlap between the 2 documents. Mathematically this
angle is expressed as:
θ(D1,D2) = arccos (
𝐷1.𝐷2
| 𝐷1 |∗| 𝐷2 |
)

43. PYTHON EXAMPLE
• https://ocw.mit.edu/ans7870/6/6.006/s08/lecturenotes/files/
docdist2.py

54. REFERENCE
• http://www.cs.haifa.ac.il/~rita/uml_course/lectures/kmeans.pdf
• https://cs.wmich.edu/alfuqaha/summer14/cs6530/lectures/ClusteringA
nalysis.pdf
• http://www.it.uu.se/edu/course/homepage/infoutv/ht09/a2t.pdf
• http://www.cse.buffalo.edu/~jing/cse601/fa12/materials/clustering_de
nsity.pdf
• Machine Learning Specialization by University of Washington in
Coursera