Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Clustering data described by categorical attributes is a challenging task in data mining applications. Unlike numerical attributes, it is difficult to define a distance beten pairs of values of a categorical attribute, since the values are not ordered. In this article,  propose a framework to learn a context-based distance for categorical attributes. The key intuition of this work is that the distance beten two values of a categorical attribute  A  i  can be determined by the way in which the values of the other attributes  A  j  are distributed in the dataset objects: if they are similarly distributed in the groups of objects in correspondence of the distinct values of  A  i  a low value of distance is obtained.  propose also a solution to the critical point of the choice of the attributes  A  j .  validate our approach by embedding our distance learning framework in a hierarchical clustering algorithm.  applied it on various real world and synthetic datasets, both low and high-dimensional. Experimental results show that our method is competitive with respect to the state of the art of categorical data clustering approaches.  also show that our approach is scalable and has a low impact on the overall computational time of a clustering task.
['Dino Ienco', 'Ruggero G. Pensa', 'Rosa Meo']
I am writing a research paper on a idea From Context to Distance: Learning Dissimilarity for Categorical Data Clustering
[{"from": "human", "value": "I am writing a research paper on a idea From Context to Distance: Learning Dissimilarity for Categorical 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: Clustering data described by categorical attributes is a challenging task in data mining applications. Unlike numerical attributes, it is difficult to define a distance beten pairs of values of a categorical attribute, since the values are not ordered. In this article,  propose a framework to learn a context-based distance for categorical attributes. The key intuition of this work is that the distance beten two values of a categorical attribute  A  i  can be determined by the way in which the values of the other attributes  A  j  are distributed in the dataset objects: if they are similarly distributed in the groups of objects in correspondence of the distinct values of  A  i  a low value of distance is obtained.  propose also a solution to the critical point of the choice of the attributes  A  j .  validate our approach by embedding our distance learning framework in a hierarchical clustering algorithm.  applied it on various real world and synthetic datasets, both low and high-dimensional. Experimental results show that our method is competitive with respect to the state of the art of categorical data clustering approaches.  also show that our approach is scalable and has a low impact on the overall computational time of a clustering task."}]