Unsupervised machine learning algorithms

Some popular examples of unsupervised machine learning algorithms are : K-mean data mining, Hideen markov, DBSCAN and Singular value decomposition. Now, let us get started and understad unsupervised learning and how they are different from each other.

K-mean data mining

The K is a powerful algorithm if you add a good pre-processing of data that removes noise.
The data should be a mixture of normal distributions in terms of fuzzy logic.
It is hard to beat k-means because there are many implementations including distributed ones.
It is a tool in the data-mining operation that is used for:-

1. Used for the audience segmentation
2. For the customer persona investigation
3. Anomaly detection.
4. In the pattern recognition.
5. The inventory management.

Hideen markov

It is called a hidden algorithm and statical model that analyze the features of data and groups.
Also called as the variation of the simple Markov chain that includes observation over the state of data.
It adds another perspective on data which gives the algorithm more points of reference.
Applications are,

1. The optical Character recognition that includes handwriting recognition.
2. Speech recognition and synthesis used for conversational user interfaces.
3. The text Classification.
4. Text Translation

They are used in data analytics operations and clustering purposes.
It will find the associations between the objects in the dataset and explores its structure and used for sound or video sources of information.

DBSCAN

DBSCAN is an algorithm used in different categories of points in a cluster points inside of the cluster.
The DBSCAN excels the practical value in clustering that needs 2 parameters in input size neighborhood and threshold of number.
It will explore the structure of information and find the elements in data.
The common element is searched from data.
The DBSCAN algorithm uses are,

1. It used in targeted Ad Content Inventory Management
2. Also for customer service personalization
3. Recommender Engines

Singular value decomposition

The Singular Value Decomposition is a useful method used in recommendation systems.
A principal component analysis is used to transform feature space from high dimensions to lower dimensions.
It helps to speed up the prediction process by removing noising data from signals, Lower dimensions data to visualize.
Singular value decomposition (SVD) is used for dimensions reduction using matrix factorization.
Kalman filtering is an unsupervised process used for signals processing for filtering noise from signals.
Clustring algorithms are like k-means clustring DBSCAN hierarchical clustering is also unsupervised algorithms.