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Comment: Added points related to DBSCAN in the table.


Volunteers

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Un-supervised

  1. Clustering -  hierarchical clustering, k-means, mixture models, DBSCAN, and OPTICS algorithm
  2. Anomaly Detection - Local Outlier Factor, and Isolation Forest
  3. Dimensionality Reduction - Principal component analysis, Independent component analysis, Non-negative matrix factorization, Singular value decomposition

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NameComments on ApplicabilityReference
Hierarchical Clustering
  1. (N-1) combination of clusters are formed to choose from.
  2. Expensive and slow. n×n  distance matrix needs to be made.
  3. Cannot work on very large datasets.
  4. Results are reproducible.
  5. Does not work well with hyper-spherical clusters.
  6. Can provide insights into the way the data pts. are clustered.
  7. Can use various linkage methods(apart from centroid).

k-means
  1. Pre-specified number of clusters.
  2. Less computationally intensive.
  3. Suited for large dataset.
  4. Point of start can be random which leads to a different result each time the algorithm runs.
  5. K-means needs circular data. Hyper-spherical clusters.
  6. K-Means simply divides data into mutually exclusive subsets without giving much insight into the process of division.
  7. K-Means uses median or mean to compute centroid for representing cluster.

Gaussian Mixture Models
  1. Pre-specified number of clusters.
  2. GMs are somewhat more flexible and with a covariance matrix we can make the boundaries elliptical (as opposed to K-means which makes circular boundaries).
  3. Another thing is that GMs is a probabilistic algorithm. By assigning the probabilities to data points, we can express how strong is our belief that a given data point belongs to a specific cluster.
  4. GMs usually tend to be slower than K-Means because it takes more iterations to reach the convergence. (The problem with GMs is that they have converged quickly to a local minimum that is not very optimal for this dataset. To avoid this issue, GMs are usually initialized with K-Means.)

DBSCAN
  1. No pre-specified no. of clusters.
  2. Computationally a little intensive.
  3. Cannot efficiently handle large datasets.
  4. Suitable for non-compact and mixed-up arbitrary shaped clusters.
  5. Uses density-based clustering. Cannot work well with density varying data points.
  6. Not effected by noise or outliers.

Reinforcement Learning

  1. Active Learning
  2. No labeled data
  3. No supervisor, only  reward
  4. Actions are sequential
  5. Feedback is delayed, not instantaneous.
  6. Can afford to make mistakes?
  7. Is it possible to use a simulated environment for the task?
  8. Lots of time
  9. Think about the variables that can define the state of the environment.
    1. State Variables and Quantify them
    2. The agent has access to these variables at every time step
    3. Concrete Reward Function and Compute Reward after action
    4. Define Policy Function

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