Integration of dense subgraph finding with feature clustering for unsupervised feature selection

نویسندگان

  • Sanghamitra Bandyopadhyay
  • Tapas Bhadra
  • Pabitra Mitra
  • Ujjwal Maulik
چکیده

In this article a dense subgraph finding approach is adopted for the unsupervised feature selection problem. The feature set of a data is mapped to a graph representation with individual features constituting the vertex set and inter-feature mutual information denoting the edge weights. Feature selection is performed in a two-phase approach where the densest subgraph is first obtained so that the features are maximally non-redundant among each other. Finally, in the second stage, feature clustering around the non-redundant features is performed to produce the reduced feature set. An approximation algorithm is used for the densest subgraph finding. Empirically, the proposed approach is found to be competitive with several state of art unsupervised feature selection algorithms. 2013 Elsevier B.V. All rights reserved.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Optimal 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 کامل

Optimal 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 کامل

Steel Consumption Forecasting Using Nonlinear Pattern Recognition Model Based on Self-Organizing Maps

Steel consumption is a critical factor affecting pricing decisions and a key element to achieve sustainable industrial development. Forecasting future trends of steel consumption based on analysis of nonlinear patterns using artificial intelligence (AI) techniques is the main purpose of this paper. Because there are several features affecting target variable which make the analysis of relations...

متن کامل

Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm

This paper describes a novel feature selection algorithm for unsupervised clustering, that combines the clustering ensembles method and the population based incremental learning algorithm. The main idea of the proposed unsupervised feature selection algorithm is to search for a subset of all features such that the clustering algorithm trained on this feature subset can achieve the most similar ...

متن کامل

A Parallel Genetic Algorithm Based Method for Feature Subset Selection in Intrusion Detection Systems

Intrusion detection systems are designed to provide security in computer networks, so that if the attacker crosses other security devices, they can detect and prevent the attack process. One of the most essential challenges in designing these systems is the so called curse of dimensionality. Therefore, in order to obtain satisfactory performance in these systems we have to take advantage of app...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Pattern Recognition Letters

دوره 40  شماره 

صفحات  -

تاریخ انتشار 2014