1. Jawad Tahsin Danish Mustafa Zaidi Kazim Zaidi Zulfiqar Hadi

2.  Classification  Clustering  Minkowski distance  K-Mean Algorithm  Similarity  Cosine based similarity  Eigen value

3.  “Classification is a data mining (machine learning) technique used to predict group membership for data instances.  In general, in classification you have a set of predefined classes and want to know which class a new object belongs to.  For example, you may wish to use classification to predict whether the weather on a particular day will be “sunny”, “rainy” or “cloudy”. Popular classification techniques include decision trees and neural networks.”

4.  Clustering is a data mining (machine learning) technique used to place data elements into related groups without advance knowledge of the group definitions.  Popular clustering techniques include k- means clustering and expectation maximization (EM) clustering.

6. Goal: Minimise the sum of the within cluster variances K stands for number of clusters Assigns data elements to the closest cluster (centre). The algorithm is iterative in nature

7. Initially, the number of clusters must be known, or chosen, to be K say. The initial step is the choose a set of K instances as centres of the clusters. Often chosen such that the points are mutually “farthest apart”, in some way. Next, the algorithm considers each instance and assigns it to the cluster which is closest. The cluster centroids are recalculated either after each instance assignment, or after the whole cycle of re-assignments. This process is iterated.

8.  Example

9.  Cosine similarity is a measure of similarity between two vectors or data points.  It is determined by measuring the cosine of the angles between the two points.  If the angle between the two points is zero then the cosine would be 1 and therefore the two entities would be perfectly similar to each other  If the angle between the two points is 90’ then the cosine would be 0 and therefore the two entities would be perfectly dissimilar to each other

10.  Similar entities  Dissimilar entities

11.  Cosine between the two points, A(x1,y1) and B(x2,y2) can be calculated by: (x1.x2)+(y1.y2) √(x1²+y1²).√(x2²+y2²)

12.  The eigenvectors of a square matrix are the non- zero vectors that, after being multiplied by the matrix, remain parallel to the original vector.  For each eigenvector, the corresponding eigenvalue is the factor by which the eigenvector is scaled when multiplied by the matrix. Av = λv where A = square matrix v = eigen vector of A λ =Scalar