![• Centroid-based clustering
In centroid-based clustering, clusters are represented by a central vector, which
may not necessarily be a member of the data set. When the number of clusters
is fixed to k, k-means clustering gives a formal definition as an optimization
problem: find the k cluster centers and assign the objects to the nearest cluster
center, such that the squared distances from the cluster are minimized.
The optimization problem itself is known to be NP-hard, and thus the common
approach is to search only for approximate solutions. A particularly well known
approximate method is Lloyd's algorithm,[8] often just referred to as "k-means
algorithm" (although another algorithm introduced this name). It does however
only find a local optimum, and is commonly run multiple times with different
random initializations. Variations of k-means often include such optimizations as
choosing the best of multiple runs, but also restricting the centroids to members
of the data set (k-medoids), choosing medians (k-medians clustering), choosing
the initial centers less randomly (k-means++) or allowing a fuzzy cluster
assignment (fuzzy c-means).](https://image.slidesharecdn.com/clusteringalgorithms-220918112722-f7edb7ce/85/Clustering-Algorithms-pptx-11-320.jpg)
Clustering Algorithms.pptx
- 1. Clustering algorithms Presented by : Issra’a Haider Hashim Depatment :Electronic & Communication Engineering
- 2. Outline: 1 What is clustering? 2 Clustering Algorithms 3 Applications
- 3. What is Clustering? Is grouping similar objects together. Is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters) . Clustering can therefore be formulated as a multi-objective optimization problem.
- 5. A BBB BBB A SIMPSONS Family SIMPSONS neighbors Children Adults
- 6. BBB BBB A A Tall People With hair Short People Hairless
- 7. “You need to know the question you are trying to answer “ - Jason Bell (2014) Bell,2014,Machine Learning: Hands-on for developers and technical professionals
- 9. •Connectivity-based clustering (hierarchical clustering): These algorithms connect "objects" to form "clusters" based on their distance. At different distances, different clusters will form, which can be represented using a dendrogram (from Greek dendro "tree" and gramma "drawing" is a tree diagram). Names Formulas Euclidean distance Manhattan distance Squared Euclidean distance Maximum distance Mahalanobis distance
- 11. • Centroid-based clustering In centroid-based clustering, clusters are represented by a central vector, which may not necessarily be a member of the data set. When the number of clusters is fixed to k, k-means clustering gives a formal definition as an optimization problem: find the k cluster centers and assign the objects to the nearest cluster center, such that the squared distances from the cluster are minimized. The optimization problem itself is known to be NP-hard, and thus the common approach is to search only for approximate solutions. A particularly well known approximate method is Lloyd's algorithm,[8] often just referred to as "k-means algorithm" (although another algorithm introduced this name). It does however only find a local optimum, and is commonly run multiple times with different random initializations. Variations of k-means often include such optimizations as choosing the best of multiple runs, but also restricting the centroids to members of the data set (k-medoids), choosing medians (k-medians clustering), choosing the initial centers less randomly (k-means++) or allowing a fuzzy cluster assignment (fuzzy c-means).
- 13. • Distribution-based clustering The clustering model most closely related to statistics is based on distribution models. Clusters can then easily be defined as objects belonging most likely to the same distribution. A convenient property of this approach is that this closely resembles the way artificial data sets are generated: by sampling random objects from a distribution.
- 14. • Density based clustering: In density-based clustering , clusters are defined as areas of higher density than the remainder of the data set. Objects in these sparse areas - that are required to separate clusters - are usually considered to be noise and border points.
- 15. Applications Biology Market research Medicine Social Science World Wide Web Others Computer science
- 16. Confused , Comment , or any questions