A Novel Design Specification Distance (DSD) based K-Mean Clustering Performance Evaluation on Engineering Materials' Database
نویسندگان
چکیده
منابع مشابه
A Novel Design Specification Distance (DSD) based K-Mean Clustering Performance Evaluation on Engineering Materials' Database
Organizing data into semantically more meaningful is one of the fundamental modes of understanding and learning. Cluster analysis is a formal study of methods for understanding and algorithm for learning. K-mean clustering algorithm is one of the most fundamental and simple clustering algorithms. When there is no prior knowledge about the distribution of data sets, K-mean is the first choice fo...
متن کاملA Novel Design Specification Distance(DSD) Based K-Mean Clustering Performace Evluation on Engineering Materials Database
Organizing data into semantically more meaningful is one of the fundamental modes of understanding and learning. Cluster analysis is a formal study of methods for understanding and algorithm for learning. K-mean clustering algorithm is one of the most fundamental and simple clustering algorithms. When there is no prior knowledge about the distribution of data sets, K-mean is the first choice fo...
متن کاملK Modes Clustering Algorithm Based on a New Distance Measure
T he leading par tit ional clustering technique, K Modes, is one of the most computationally eff icient clustering methods fo r categ orical data. In the t raditional K Modes algo rithm, the simple matching dissim ilarity measure is used to compute the distance betw een two values of the same catego rical at t ributes. T his compares tw o categorical v alues directly and results in either a dif...
متن کاملA Novel Approach Towards K-Mean Clustering Algorithm With PSO
In this paper, the proposed approach is an unique combination of two most popular clustering algorithms Particle Swarm Optimization (PSO) and K-Means to achieve better clustering result. Clustering is a technique of grouping homogeneous objects of a dataset with aim to extract some meaningful pattern or information. K-Means algorithm is the most popular clustering algorithm because of its easy ...
متن کاملK-mean Based Clustering and Context Quantization
In this thesis, we study the problems of K-means clustering and context quantization. The main task of K-means clustering is to partition the training patterns into k distinct groups or clusters that minimize the mean-square-error (MSE) objective function. But the main difficulty of conventional K-means clustering is that its classification performance is highly susceptible to the initialized s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2012
ISSN: 0975-8887
DOI: 10.5120/8832-3043