K Means Algorithm
In partitioning method the the ‘n’ is the object group and that group are combined with group notation ‘K’, where each partition represent one cluster. In partitioning method the cluster is a group of undefined or objects with similar characteristics.
There are two classified partitioning method are as follows-
- K means
- k mediads
k-means is one of the simplest unsupervised learning algorithm. Which follows a easy way and simplest formation for defining clustering from undefined objects. K means algorithm used for divide ‘X’ in different cluster and shows which is suitable for X” and accurate cluster to and denote accurate cluster for predict a value.
Advantages of K means algorithm-
- It is fast and easy to understand.
- Gives a better result when objects are different from each others.
Disadvantages for understanding k-means algorithm-
- It is unable to handle noisy data.
- That algorithm are fails for Non linear data set.