Comments on "modified K-means algorithm for vector quantizer design"
نویسندگان
چکیده
منابع مشابه
Comments on " Modified K - means algorithm for vector quantizer design
Recently a modified -means algorithm for vector quantization design has been proposed where the codevector updating step is as follows: new codevector = current codevector + scale factor (new centroid current codevector). This algorithm uses a fixed value for the scale factor. In this paper, we propose the use of a variable scale factor which is a function of the iteration number. For the vecto...
متن کاملComments on “ Modified - Means Algorithm for Vector Quantizer Design ”
Recently a modified -means algorithm for vector quantization design has been proposed where the codevector updating step is as follows: new codevector = current codevector + scale factor (new centroid current codevector). This algorithm uses a fixed value for the scale factor. In this paper, we propose the use of a variable scale factor which is a function of the iteration number. For the vecto...
متن کاملAn algorithm for uniform vector quantizer design
A vector quantizer maps a k-dimensional vector into one of a finite set of output vectors or “points”. Although certain lattices have been shown to have desirable properties for vector quantization applications, there are as yet no algorithms available in the quantization literature for building quantizers based on these lattices. An algorithm for designing vector quantizers based on the root l...
متن کاملModified K-Means Algorithm for Genetic Clustering
The K-Means Clustering Approach is one of main algorithms in the literature of Pattern recognition and Machine Learning. Yet, due to the random selection of cluster centers and the adherence of results to initial cluster centers, the risk of trapping into local optimality ever exists. In this paper, inspired by a genetic algorithm which is based on the K-means method , a new approach is develop...
متن کاملPersistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm
Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K...
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: IEEE Transactions on Image Processing
سال: 2000
ISSN: 1057-7149,1941-0042
DOI: 10.1109/83.877216