A Modified K-means Algorithms - Bi-Level K-Means Algorithm
2014
In this paper, a modified K-means algorithm is proposed to categorize a set of data into smaller clusters. K- means algorithm is a simple and easy clustering method which can efficiently separate a huge number of continuous numerical data with high-dimensions. Moreover, the data in each cluster are similar to one another. However, it is vulnerable to outliers and noisy data, and it spends much executive time in partitioning data too. Noisy data, outliers, and the data with quite different values in one cluster may reduce the accuracy rate of data clustering since the cluster center cannot precisely describe the data in the cluster. In this paper, a bi-level K-means algorithm is hence provided to solve the problems mentioned above. The bi-level K-means algorithm can give an expressive experimental results.
Keywords:
- Machine learning
- Data stream clustering
- k-medians clustering
- Determining the number of clusters in a data set
- Cluster analysis
- Nearest-neighbor chain algorithm
- Computer science
- FSA-Red Algorithm
- CURE data clustering algorithm
- Canopy clustering algorithm
- Algorithm
- Pattern recognition
- Artificial intelligence
- Fuzzy clustering
- k-means clustering
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