Model-based multidimensional clustering of categorical data
نویسندگان
چکیده
منابع مشابه
Model-based multidimensional clustering of categorical data
Existing models for cluster analysis typically consist of a number of attributes that describe the objects to be partitioned and one single latent variable that represents the clusters to be identified. When one analyzes data using such a model, one is looking for one way to cluster data that is jointly defined by all the attributes. In other words, one performs unidimensional clustering. This ...
متن کاملFast Multidimensional Clustering of Categorical Data
Early research work on clustering usually assumed that there was one true clustering of data. However, complex data are typically multifaceted and can be meaningfully clustered in many different ways. There is a growing interest in methods that produce multiple partitions of data. One such method is based on latent tree models (LTMs). This method has a number of advantages over alternative meth...
متن کاملClustering-Based Categorical Data Protection
The need of improving the privacy on public datasets is becoming more and more important because the number of public available datasets is growing very fast. This forced the continuous research to find better protection methods that prevent the disclosure of the entities or individuals in a dataset while preserving the data utility. In this paper we present a new approach for categorical data ...
متن کاملModel-Based Clustering for Conditionally Correlated Categorical Data
An extension of the latent class model is proposed for clustering categorical data by relaxing the classical class conditional independence assumption of variables. In this model, variables are grouped into inter-independent and intra-dependent blocks in order to consider the main intra-class correlations. The dependence between variables grouped into the same block of a class is taken into acc...
متن کاملDistance based Clustering for Categorical Data
Learning distances from categorical attributes is a very useful data mining task that allows to perform distance-based techniques, such as clustering and classification by similarity. In this article we propose a new context-based similarity measure that learns distances between the values of a categorical attribute (DILCA DIstance Learning of Categorical Attributes). We couple our similarity m...
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: Artificial Intelligence
سال: 2012
ISSN: 0004-3702
DOI: 10.1016/j.artint.2011.09.003