作者:Anil K·Jain/Richard C·Dubes 出版社:Prentice Hall 出版年:1988 页数:320 ISBN:9780130222787 豆瓣评分 目前无人评价 评价: 内容简介· ··· dendrogram, threshold graph, cluster analysis, multidimensional scaling, random graph, hierarchical clustering, eigenvectors, Euclidean distance, eigenvalues...
Algorithms For Clustering DataCluster analysisAlgorithmsComputer algorithmsAn abstract is not available.doi:10.1080/00401706.1990.10484648Partitional ClusteringPrentice Hall
Clustering is the process of grouping together objects that are similar. The similarity between objects is evaluated by using a several types of dissimilarities (particularly, metrics and ultrametrics). After discussing partitions and dissimilarities, two basic mathematical concepts important for clustering...
An Efficient Method of Partitioning High Volumes of Multidimensional Data for Parallel Clustering Algorithms An optimal data partitioning in parallel & distributed implementation of clustering algorithms is a necessary computation as it ensures independent task completion, fair distribution, less number of aff...
C. (1988). Algorithms for clustering data. Upper Saddle River, NJ: Prentice-Hall Inc. MATH Google Scholar Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. Proceedings of the 10th European Conference on Machine Learning, 1398, 137–14...
However, data with both attributes or mixed data exists universally in real life. K-prototype is a well-known algorithm for clustering mixed data because of its effectiveness in handling large data. However, practically, k-prototype has two main weaknesses, the use of mode as a cluster center...
Python implementations of the k-modes and k-prototypes clustering algorithms. Relies on numpy for a lot of the heavy lifting. k-modes is used for clustering categorical variables. It defines clusters based on the number of matching categories between data points. (This is in contrast to the mo...
Data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along...
There’s a whole range of clustering algorithms, each one with its pros and cons regarding what type of data they with, time complexity, weaknesses, and so on. To mention more algorithms, for example there’s Hierarchical Agglomerative Clustering (or Linkage Clustering), good for when we don...
Helper functions are included in cluster.py and data_generation_playground.py, and can be imported in the standard Python way. How do I use your code for [METHOD X]? A great many analysis options for dimensionality reduction and clustering have already been implemented, and you can use this...