Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A few techniques of rough categorical data clustering exist to group objects#R##N#having similar characteristics. Hover, the performance of the techniques is an#R##N#issue due to low accuracy, high computational complexity and clusters purity.#R##N#This work proposes a new technique called Maximum Dependency Attributes#R##N#(MDA) to improve the previous techniques due to these issues. The proposed#R##N#technique is based on rough set theory by taking into account the dependency of#R##N#attributes of an information system. The main contribution of this technique is to#R##N#introduce a new technique to classify objects from categorical datasets which has#R##N#better performance as compared to the baseline techniques.#R##N#The algorithm of the proposed technique is implemented in MATLAB®#R##N#version 7.6.0.324 (R2008a). They are executed sequentially on a processor Intel Core#R##N#2 Duo CPUs. The total main memory is 1 Gigabyte and the operating system is#R##N#Windows XP Professional SP3. Results collected during the experiments on four#R##N#small datasets and thirteen UCI benchmark datasets for selecting a clustering#R##N#attribute show that the proposed MDA technique is an efficient approach in terms of#R##N#accuracy and computational complexity as compared to BC, TR and MMR#R##N#techniques. For the clusters purity, the results on Soybean and Zoo datasets show that#R##N#MDA technique provided better purity up to 17% and 9%, respectively.#R##N#The experimental result on supplier chain management clustering also#R##N#demonstrates how MDA technique can contribute to practical system and establish#R##N#the better performance for computation complexity and clusters purity up to 90% and#R##N#23%, respectively.
['Tutut Herawan', 'Iwan Tri Riyadi Yanto', 'Mustafa Mat Deris']
I am writing a research paper on a idea Rough Set Approach for Categorical Data Clustering
[{"from": "human", "value": "I am writing a research paper on a idea Rough Set Approach 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: A few techniques of rough categorical data clustering exist to group objects#R##N#having similar characteristics. Hover, the performance of the techniques is an#R##N#issue due to low accuracy, high computational complexity and clusters purity.#R##N#This work proposes a new technique called Maximum Dependency Attributes#R##N#(MDA) to improve the previous techniques due to these issues. The proposed#R##N#technique is based on rough set theory by taking into account the dependency of#R##N#attributes of an information system. The main contribution of this technique is to#R##N#introduce a new technique to classify objects from categorical datasets which has#R##N#better performance as compared to the baseline techniques.#R##N#The algorithm of the proposed technique is implemented in MATLAB\u00ae#R##N#version 7.6.0.324 (R2008a). They are executed sequentially on a processor Intel Core#R##N#2 Duo CPUs. The total main memory is 1 Gigabyte and the operating system is#R##N#Windows XP Professional SP3. Results collected during the experiments on four#R##N#small datasets and thirteen UCI benchmark datasets for selecting a clustering#R##N#attribute show that the proposed MDA technique is an efficient approach in terms of#R##N#accuracy and computational complexity as compared to BC, TR and MMR#R##N#techniques. For the clusters purity, the results on Soybean and Zoo datasets show that#R##N#MDA technique provided better purity up to 17% and 9%, respectively.#R##N#The experimental result on supplier chain management clustering also#R##N#demonstrates how MDA technique can contribute to practical system and establish#R##N#the better performance for computation complexity and clusters purity up to 90% and#R##N#23%, respectively."}]