Properties of cluster-

Property 1: All the data points in a cluster should be similar to each other.

Property 2: The data points from different clusters should be as different as possible

**Spectral Clustering and GMM**

**How does Spectral Clustering work?**

In spectral clustering, the data points are treated as nodes of a graph. Thus, clustering is treated as a graph partitioning problem. The nodes are then mapped to a low-dimensional space that can be easily segregated to form clusters. **…**

**METHODS FOR DETERMINING OPTIMAL NUMBER OF CLUSTERS:**

**ELBOW METHOD:**

A fundamental step for any unsupervised algorithm is to determine the optimal number of clusters into which the data may be clustered. The Elbow Method is one of the most popular methods to determine this optimal value of k.

**Support Vector Machines**

- In machine learning,
**support-vector machines**(**SVMs**, also**support-vector networks**) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.An …

**HIERARCHICAL AGGLOMERATIVE CLUSTERING:**

**Implementation code in python:**

https://github.com/mrinalyadav7-atom/Text_clustering-Numo-Uno-/tree/master/Agglomerative

Also known as bottom-up approach or hierarchical agglomerative clustering (HAC). A structure that is more informative than the unstructured set of clusters returned by flat clustering. This clustering algorithm does not require us to prespecify the number of clusters. …

**AFFINITY PROPAGATION:**

- We use a type of clustering algorithm where the complete data is viewed as a network with each data point being a node in the network.
- The entire algorithm is based on finding iteratively how well one point is suited to be a representative of another point (i.e…

**WORD EMBEDDINGS-NLP**

Word Embeddings are the texts converted into numbers and there may be different numerical representations of the same text.

**” Word Embeddings are Word converted into numbers ”**

**A dictionary may be the list of all unique words in the sentence. …**

**DBSCAN Clustering Algorithm**

*DBSCAN ALGORITHM:*

- Based on a set of points (let’s think in a bidimensional space as exemplified in the figure), DBSCAN groups together points that are close to each other based on a distance measurement (usually Euclidean distance) and a minimum number of points. …