Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Unlike traditional clustering methods that focus on grouping objects with similar values on a set of dimensions, clustering by pattern similarity finds objects that exhibit a coherent pattern of rise and fall in subspaces. Pattern-based clustering extends the concept of traditional clustering and benefits a wide range of applications, including large scale scientific data analysis, target marketing, b usage analysis, etc. Hover, state-of-the-art pattern-based clustering methods (e.g., the pCluster algorithm) can only handle data sets of thousands of records, which makes them inappropriate for many real-life applications. Furthermore, besides the huge data volume, many data sets are also characterized by their sequentiality, for instance, customer purchase records and network event logs are usually modeled as data sequences. Hence, it becomes important to enable pattern-based clustering methods i) to handle large datasets, and ii) to discover pattern similarity embedded in data sequences. ,  present a novel algorithm that offers this capability. Experimental results from both real life and synthetic datasets prove its effectiveness and efficiency.
['Haixun Wang', 'Fang Chu', 'Wei Fan', 'Philip S. Yu', 'Jian Pei']
I am writing a research paper on a idea A fast algorithm for subspace clustering by pattern similarity
[{"from": "human", "value": "I am writing a research paper on a idea A fast algorithm for subspace clustering by pattern similarity"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Unlike traditional clustering methods that focus on grouping objects with similar values on a set of dimensions, clustering by pattern similarity finds objects that exhibit a coherent pattern of rise and fall in subspaces. Pattern-based clustering extends the concept of traditional clustering and benefits a wide range of applications, including large scale scientific data analysis, target marketing, b usage analysis, etc. Hover, state-of-the-art pattern-based clustering methods (e.g., the pCluster algorithm) can only handle data sets of thousands of records, which makes them inappropriate for many real-life applications. Furthermore, besides the huge data volume, many data sets are also characterized by their sequentiality, for instance, customer purchase records and network event logs are usually modeled as data sequences. Hence, it becomes important to enable pattern-based clustering methods i) to handle large datasets, and ii) to discover pattern similarity embedded in data sequences. ,  present a novel algorithm that offers this capability. Experimental results from both real life and synthetic datasets prove its effectiveness and efficiency."}]