Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Proposal of new hybrid approach of clustering.Benchmarked with several synthetic and real datasets.Performs ll compared to a few other similar ll performing algorithms.Hybrid algorithm shown to be better than its components. Clustering algorithms have regained momentum with recent popularity of data mining and knowledge discovery approaches. To obtain good clustering in reasonable amount of time, various meta-heuristic approaches and their hybridization, sometimes with K-Means technique, have been employed. A Kalman Filtering based heuristic approach called Heuristic Kalman Algorithm (HKA) has been proposed a few years ago, which may be used for optimizing an objective function in data/feature space.  at first HKA is employed in partitional data clustering. Then an improved approach named HKA-K is proposed, which combines the benefits of global exploration of HKA and the fast convergence of K-Means method. Implemented and tested on several datasets from UCI machine learning repository, the results obtained by HKA-K re compared with other hybrid meta-heuristic clustering approaches. It is shown that HKA-K is atleast as good as and often better than the other compared algorithms.
['Arjun Pakrashi', 'B. B. Chaudhuri']
I am writing a research paper on a idea A Kalman filtering induced heuristic optimization based partitional data clustering
[{"from": "human", "value": "I am writing a research paper on a idea A Kalman filtering induced heuristic optimization based partitional data clustering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Proposal of new hybrid approach of clustering.Benchmarked with several synthetic and real datasets.Performs ll compared to a few other similar ll performing algorithms.Hybrid algorithm shown to be better than its components. Clustering algorithms have regained momentum with recent popularity of data mining and knowledge discovery approaches. To obtain good clustering in reasonable amount of time, various meta-heuristic approaches and their hybridization, sometimes with K-Means technique, have been employed. A Kalman Filtering based heuristic approach called Heuristic Kalman Algorithm (HKA) has been proposed a few years ago, which may be used for optimizing an objective function in data/feature space.  at first HKA is employed in partitional data clustering. Then an improved approach named HKA-K is proposed, which combines the benefits of global exploration of HKA and the fast convergence of K-Means method. Implemented and tested on several datasets from UCI machine learning repository, the results obtained by HKA-K re compared with other hybrid meta-heuristic clustering approaches. It is shown that HKA-K is atleast as good as and often better than the other compared algorithms."}]