Incremental Fuzzy Clustering Algorithm For Large Datasets
Keywords:
Incremental Fuzzy c-Means Double clustering, Fuzzy c-Means, Silhouette, Davies-Bouldin, Calinski HarabaszAbstract
Clustering streaming data poses unique challenges that are different from traditional batch processing. Conventional clustering methods struggle with the high volume, speed, and diversity of data due to limitations in memory, computing power, and processing time. The challenges of clustering large datasets include limited storage capacity, the requirement to process the data in a single pass, and the concept drift of the data. A novel method, incremental fuzzy double clustering (IFDC) has been proposed to tackle these challenges. IFDC is an innovative version of Fuzzy c-Means (FCM) and incremental clustering. It divides the data into groups based on memory capacity and clusters them. Relevant samples from each group are selected using stratified sampling and k-Medoid methods and then transferred to the next group. This process carries the essence of the data from the beginning to the end. The newly reached dataset can be easily merged with the last block of data and clustered, instead of clustering the entire dataset as it arrives. The performance of IFDC was evaluated using Silhouette, Davies-Bouldin, and Calinski Harabasz Indexes, and the results demonstrate that IFDC outperforms traditional techniques such as FCM and k-means by successfully overcoming the challenges of clustering large data. The benefits of IFDC include improved efficiency and reduced clustering time. It efficiently manages large streaming datasets by continuously accommodating new data and utilizing sampling methods, thereby enhancing accuracy, reducing execution time, and eliminating the need for complete re-clustering.
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