Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Clustering of big data has received much attention recently. ,  present a new clusiVAT algorithm and compare it with four other popular data clustering algorithms. Three of the four comparison methods are based on the ll known, classical batch    ${k}$   -means model. Specifically,  use    ${k}$   -means, single pass    ${k}$   -means, online    ${k}$   -means, and clustering using representatives (CURE) for numerical comparisons. clusiVAT is based on sampling the data, imaging the reordered distance matrix to estimate the number of clusters in the data visually, clustering the samples using a relative of single linkage (SL), and then noniteratively extending the labels to the rest of the data-set using the nearest prototype rule. Previous work has established that clusiVAT produces true SL clusters in compact-separated data.  have performed experiments to show that    ${k}$   -means and its modified algorithms suffer from initialization issues that cause many failures. On the other hand, clusiVAT needs no initialization, and almost always finds partitions that accurately match ground truth labels in labeled data. CURE also finds SL type partitions but is much slor than the other four algorithms. In our experiments, clusiVAT proves to be the fastest and most accurate of the five algorithms; e.g., it recovers 97% of the ground truth labels in the real world KDD-99 cup data (4 292 637 samples in 41 dimensions) in 76 s.
['Dheeraj Kumar', 'James C. Bezdek', 'Marimuthu Palaniswami', 'Sutharshan Rajasegarar', 'Christopher Leckie', 'Timothy C. Havens']
I am writing a research paper on a idea A Hybrid Approach to Clustering in Big Data
[{"from": "human", "value": "I am writing a research paper on a idea A Hybrid Approach to Clustering in Big Data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Clustering of big data has received much attention recently. ,  present a new clusiVAT algorithm and compare it with four other popular data clustering algorithms. Three of the four comparison methods are based on the ll known, classical batch    ${k}$   -means model. Specifically,  use    ${k}$   -means, single pass    ${k}$   -means, online    ${k}$   -means, and clustering using representatives (CURE) for numerical comparisons. clusiVAT is based on sampling the data, imaging the reordered distance matrix to estimate the number of clusters in the data visually, clustering the samples using a relative of single linkage (SL), and then noniteratively extending the labels to the rest of the data-set using the nearest prototype rule. Previous work has established that clusiVAT produces true SL clusters in compact-separated data.  have performed experiments to show that    ${k}$   -means and its modified algorithms suffer from initialization issues that cause many failures. On the other hand, clusiVAT needs no initialization, and almost always finds partitions that accurately match ground truth labels in labeled data. CURE also finds SL type partitions but is much slor than the other four algorithms. In our experiments, clusiVAT proves to be the fastest and most accurate of the five algorithms; e.g., it recovers 97% of the ground truth labels in the real world KDD-99 cup data (4 292 637 samples in 41 dimensions) in 76 s."}]