# Pattern Recognition

- **What is Pattern Recognition ?**
`process of recognizing patterns by using machine learning algorithm. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation. One of the important aspects of the pattern recognition is its application potential.`

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- **What is PCA ?**
`a statistical procedure that uses an orthogonal transformation which converts a set of correlated variables to a set of uncorrelated variables.`

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- **What is K-Means ?**
`groups the unlabeled dataset into different clusters. Here K defines the number of pre-defined clusters that need to be created in the process. It is a centroid-based algorithm, where each cluster is associated with a centroid. The main aim of this algorithm is to minimize the sum of distances between the data point and their corresponding clusters.`

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- **What is K-Nearest Neighbour ?**
`simple, supervised machine learning algorithm that can be used to solve both classification and regression problems. It's easy to implement and understand, but has a major drawback of becoming significantly slows as the size of that data in use grows.`

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- **Data Preprocessing Steps ?**
`data cleaning, data integration, data reduction, and data transformation`

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- **What is SVD ?**
`Singular Value Decomposition is a technique used for dimensionality reduction but it is not linked to any particular statistical method. It simply states that rectangular matrix A can be broken down into three products of matrices i.e. Orthogonal matrix (U), diagonal matrix (S), and transpose of orthogonal matrix (V).`

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- **What is Branch and Bound ?**
`an algorithm design paradigm which is generally used for solving combinatorial optimization problems.`

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- **What is SVM ?**
`Support vector machine. Works on classification problem to classify in 2 categories. Works well with multidimensional data.`

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- **How to calculate 'K' of K-Means ?**
`Elbow Method. uses the concept of WCSS value. WCSS stands for Within Cluster Sum of Squares, which defines the total variations within a cluster.`
- It executes the K-means clustering on a given dataset for different K values (ranges from 1-10).
- For each value of K, calculates the WCSS value.
Plots a curve between calculated WCSS values and the number of clusters K.
- The sharp point of bend or a point of the plot looks like an arm, then that point is considered as the best value of K.

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- **What is Euclidean Distance ?**
`length of a line segment between the two points.can be calculated from the Cartesian coordinates of the points using the Pythagorean theorem, therefore occasionally being called the Pythagorean distance.`

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