Cluster Analysis Flashcards

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1
Q

What is Cluster Analysis?

A

is a multivariate statistical technique that groups observations on the basis some of their features or variables they are described by

observations in a dataset can be divided into different groups

example: clustering by geographic proximity
or language
or Market Segmentation

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2
Q

What is the goal of Cluster Analysis?

A

To maximize the similarity of observations within a cluster and maximize the dissimilarity between clusters

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3
Q

When is clustering most often used?

A

is often used as a preliminary step in all types of analysis

it is a useful technique for exploring and identifying patterns in the data

Data Scientists often turn to it when they have no idea where to start or what to expect

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4
Q

What is a key distinguishing trait of supervised leanering?

A

We are dealing with labeled data

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5
Q

What is the Euclidean distance?

A
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6
Q

What is a Centroid?

A

the mean position of a group of points

aka - center of mass

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7
Q

What does K in K-means clustering stand for?

A

The number of clusters

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8
Q

What is the proper way of selecting the number of clusters?

A

The elbow method

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9
Q

What is Clustering about?

A
  1. Minimizing the distance between points in a cluster
  2. Maximizing the distance between clusters
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10
Q

What does WCSS stand for?

A

Within-cluster sum of squares

if we minimize WCSS we have reached the perfect clustering solution

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11
Q

What are pros of K-Means Clustering?

A
  1. Simple to understand
  2. Fast to cluster
  3. Widely available
  4. Easy to implement
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12
Q

What are some cons of K-means Clustering?

A
  1. We need to pick K
  2. Sensitive to initialization
  3. Sensitive to outliers
  4. Produces spherical solutions
  5. Standardization
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13
Q

What are the 3 Types of Analysis?

A
  1. Exploratory
  2. Confirmatory
  3. Explanatory
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14
Q

What are characteristics of Exploratory Analysis?

A
  • Getting acquainted with the data
  • Search for patterns
  • Plan - determining what methods may be useful to investigate further
    ie. Data Visualization, Descriptive Stats ( pd.describe() ), Clustering
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15
Q

What are characteristics of Confirmatory and Explanatory Analysis?

A
  • Explain a phenomenon
  • Confirm a hypothesis
  • Validate previous research

using hypothesis testing and regression analysis

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16
Q

What are the two broad types of clustering?

A
  1. Flat ie K-Means
  2. Hierarchical
17
Q

What are the two types of Hierarchical Clustering?

A
  1. Agglomerative (bottom-up)
  2. Divisive (top-down)