Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Clustering with pairwise constraints has received much attention in the clustering community recently. Particularly, must-link and cannot-link constraints beten a given pair of instances in the data set are common prior knowledge incorporated in many clustering algorithms today. This approach has been shown to be successful in guiding a number of famous clustering algorithms towards more accurate results. Hover, recent work has also shown that the incorporation of must-link and cannot-link constraints makes clustering algorithms too much sensitive to “the assignment order of instances” and therefore results in consequent constraint violation. The major contributions of this paper are two folds. One is to address the issue of constraint violation in Cop-Kmeans by emphasizing a sequenced assignment of cannot-link instances after conducting a Breadth-First Search of the cannot-link set. The other is to reduce the computational complexity of Cop-Kmeans for massive data sets by adopting a MapReduce Framework. Experimental results show that our approach performs ll on massive data sets while may overcome the problem of constraint violation.
['Yan Yang', 'Tonny Rutayisire', 'Chao Lin', 'Tianrui Li', 'Fei Teng']
I am writing a research paper on a idea An Improved Cop-Kmeans Clustering for Solving Constraint Violation Based on MapReduce Framework
[{"from": "human", "value": "I am writing a research paper on a idea An Improved Cop-Kmeans Clustering for Solving Constraint Violation Based on MapReduce Framework"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Clustering with pairwise constraints has received much attention in the clustering community recently. Particularly, must-link and cannot-link constraints beten a given pair of instances in the data set are common prior knowledge incorporated in many clustering algorithms today. This approach has been shown to be successful in guiding a number of famous clustering algorithms towards more accurate results. Hover, recent work has also shown that the incorporation of must-link and cannot-link constraints makes clustering algorithms too much sensitive to \u201cthe assignment order of instances\u201d and therefore results in consequent constraint violation. The major contributions of this paper are two folds. One is to address the issue of constraint violation in Cop-Kmeans by emphasizing a sequenced assignment of cannot-link instances after conducting a Breadth-First Search of the cannot-link set. The other is to reduce the computational complexity of Cop-Kmeans for massive data sets by adopting a MapReduce Framework. Experimental results show that our approach performs ll on massive data sets while may overcome the problem of constraint violation."}]