The objective of clustering is to partition a set of data objects into clusters such that data objects in the same cluster are more similar to each other than ...
People also ask
Which clustering algorithm works well for mixed type data categorical and numerical?
Can k-means algorithm be applied to both categorical and numerical variables?
What is K-modes clustering algorithm for categorical data?
What is fuzzy K clustering algorithm?
In this paper, first, we combine mean and fuzzy centroid to represent the prototype of a cluster, and employ a new measure based on co-occurrence of values to ...
A partition-based clustering algorithm for mixed data is presented, using the multi-Modes representation means of category centres of categorical data in ...
Jun 1, 2012 · In this paper, first, we combine mean and fuzzy centroid to represent the prototype of a cluster, and employ a new measure based on co- ...
This paper presents a clustering algorithm based on k-mean paradigm that works well for data with mixed numeric and categorical features. We propose new cost ...
A fuzzy k-prototypes algorithm integrating k-means and k-modes algorithm is presented and is used to mixed databases. Experiments on several real databases ...
Jan 17, 2021 · K-Prototype is a clustering method based on partitioning. Its algorithm is a improvement form of the K-Means and K-Mode clustering ...
Missing: fuzzy | Show results with:fuzzy
Feb 3, 2023 · Bibliographic details on A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data.
A new hybrid dissimilarity coefficient for k-prototypes algorithm is proposed, which can be applied to the data with numerical, categorical and mixed ...
We have developed probabilistic distance measure to compute significance of attributes for numeric data, and distance between two categorical values. We used ...