Downloaded 2,600 times
Uploaded byKasun Ranga Wijeweera
K means Clustering Algorithm
K-means clustering is an algorithm that groups data points into k number of clusters based on their similarity. It works by randomly selecting k data points as initial cluster centroids and then assigning each remaining point to the closest centroid. It then recalculates the centroids and reassigns points in an iterative process until centroids stabilize. While efficient, k-means clustering has weaknesses in that it requires specifying k, can get stuck in local optima, and is not suitable for non-convex shaped clusters or noisy data.
K means Clustering Algorithm
- 1. K-means clustering algorithm Kasun Ranga Wijeweera (krw19870829@gmail.com)
- 2. What is Clustering? •Organizing data into classes such that there is • high intra-class similarity • low inter-class similarity • Finding the class labels and the number of classes directly from the data (in contrast to classification). • More informally, finding natural groupings among objects.
- 3. What is anatural grouping among these objects?
- 4. What is anatural grouping among these objects? Clustering is subjective Simpson's Family School Employees Females Males
- 5. Defining Distance Measures Definition:Let O1 and O2 be two objects from the universe of possible objects. The distance (dissimilarity) between O1 and O2 is a real number denoted by D(O1,O2) Kasun Kiosn 0.23 3 342.7
- 6. Consider a Setof Data Points, And a Set of Clusters,
- 7.
- 8. Algorithm k-means 1. Randomlychoose K data items from X as initial centroids. 2. Repeat Assign each data point to the cluster which has the closest centroid. Calculate new cluster centroids. Until the convergence criteria is met.
- 9.
- 10.
- 11.
- 12.
- 13.
- 14. K-means gets stuckin a local optima
- 15.
- 16.
- 17.
- 18.
- 19.
- 20.
- 21. Applications of K-meansMethod • Optical Character Recognition • Biometrics • Diagnostic Systems • Military Applications
- 22. Comments on theK-Means Method • Strength – Relatively efficient: O(tkn), where n is # objects, k is # clusters, and t is # iterations. Normally, k, t << n. – Often terminates at a local optimum. The global optimum may be found using techniques such as: deterministic annealing and genetic algorithms • Weakness – Applicable only when mean is defined, then what about categorical data? – Need to specify k, the number of clusters, in advance – Unable to handle noisy data and outliers – Not suitable to discover clusters with non-convex shapes
- 23.
- 24. Thanks for yourattention !