Innovative Teaching-Learning Process: Categorical Clustering Data
نویسندگان
چکیده
منابع مشابه
Clustering Categorical Data
Dynamical systems approach for clustering categorical data have been studied by some authors [1]. However, the proposed dynamic algorithm cannot guarantee convergence, so that the execution may get into an in nite loop even for very simple data. We de ne a new conguration updating algorithm for clustering categorical data sets. Let us consider a relational table with k elds, each of which can a...
متن کاملClustering categorical data streams
The data stream model has been defined for new classes of applications involving massive data being generated at a fast pace. Web click stream analysis and detection of network intrusions are two examples. Cluster analysis on data streams becomes more difficult, because the data objects in a data stream must be accessed in order and can be read only once or few times with limited resources. Rec...
متن کاملContext-Based Distance Learning for Categorical Data Clustering
Clustering data described by categorical attributes is a challenging task in data mining applications. Unlike numerical attributes, it is difficult to define a distance between pairs of values of the same categorical attribute, since they are not ordered. In this paper, we propose a method to learn a context-based distance for categorical attributes. The key intuition of this work is that the d...
متن کاملClustering Data with Categorical Relationships
data aims at considering numeric data, categorical data or a mixture of both. For such data, the concentration was finding a relationship between the data points to be clustered. The relationships were limited to being either binary or fuzzy. Both involved a numeric value called distance or any other similarity measure between two data points and cluster them together if found similar. With tim...
متن کاملClustering From Categorical Data Sequences
The three-parameter cluster model is a combinatorial stochastic process that generates categorical response sequences by randomly perturbing a fixed clustering parameter. This clear relationship between the observed data and the underlying clustering is particularly attractive in cluster analysis, in which supervised learning is a common goal and missing data is a familiar issue. The model is w...
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: Journal of Engineering Education Transformations
سال: 2020
ISSN: 2394-1707,2349-2473
DOI: 10.16920/jeet/2020/v33i0/150207