Clustering and variable selection for categorical multivariate data
نویسندگان
چکیده
منابع مشابه
Variable selection for multivariate failure time data.
In this paper, we proposed a penalised pseudo-partial likelihood method for variable selection with multivariate failure time data with a growing number of regression coefficients. Under certain regularity conditions, we show the consistency and asymptotic normality of the penalised likelihood estimators. We further demonstrate that, for certain penalty functions with proper choices of regulari...
متن کاملVariable Selection for Multivariate Survival data
It is assumed for the Cox’s proportional hazards model that the survival times of subjects are independent. This assumption might be violated in some situations, in which the collected data are correlated. The well-known Cox model is not valid in this situation because independence assumption among individuals is violated. For this purpose Cox’s proportional hazard model is extent to the analys...
متن کاملClustering Ensembles for Categorical Data
Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature. In this paper we focus on the design of ensembles for categorical data. Our approach leverages diverse input clusterings discovered in random subspaces. We experimentally demostrate the efficacy of our technique in combination with the categorical clustering algorithm COOLCAT.
متن کاملData Reduction Method for Categorical Data Clustering
Categorical data clustering constitutes an important part of data mining; its relevance has recently drawn attention from several researchers. As a step in data mining, however, clustering encounters the problem of large amount of data to be processed. This article offers a solution for categorical clustering algorithms when working with high volumes of data by means of a method that summarizes...
متن کاملOptimal Feature Selection for Data Classification and Clustering: Techniques and Guidelines
In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...
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: Electronic Journal of Statistics
سال: 2013
ISSN: 1935-7524
DOI: 10.1214/13-ejs844