Machine-learning solutions to classification, clustering and matching problems critically depend on the adopted metric, which in the past was selected heuristically. In the last decade, it has been demonstrated that an appropriate metric can be learnt from data, resulting in superior performance as compared with traditional metrics. This has recently stimulated a considerable interest in the to...

artificial immune systems (ais) can be defined as soft computing systems inspired by immune system of vertebrates. immune system is an adaptive pattern recognition system. ais have been used in pattern recognition, machine learning, optimization and clustering. feature reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encoun...

The paper aims at proposing the fast clustering algorithm for eliminating irrelevant and redundant data. Feature selection is applied to reduce the number of features in many applications where data has hundreds or thousands of features. Existing feature selection methods mainly focus on finding relevant features. In this paper, we show that feature relevance alone is insufficient for efficient...

Feature selection algorithms eliminate irrelevant and redundant features, even the noise, while preserving the most representative features. They can reduce the dimension of the dataset, extract essential features in high dimensional data and improve learning quality. Existing feature selection algorithms are all carried out in data space. However, the information of feature space cannot be ful...

Nowadays, academic researchers maintain a personal library of papers, which they would like to organize based on their needs, e.g., research, projects, or courseware. Clustering techniques are often employed to achieve this goal by grouping the document collection into different topics. Unsupervised clustering does not require any user effort but only produces one universal output with which us...

Text clustering belongs to the unsupervised machine learning, the discriminability of class attributes cannot be measured in clustering. And the traditional text feature selection methods cannot effectively solve the high-dimensional problem. To overcome the weakness in existing feature selection, this paper proposes a new method which introduces the cloud model theory into feature selection, c...

In mining, document clustering pretends to diminish the size by constructing model which is extremely essential in various web-based applications. Over past few decades, mining approaches are analysed and evaluated enhance process of attain better results; however, most cases, documents messed up degrade performance reducing level accuracy. The data instances need be organized a productive summ...

Clustering is an important machine learning task that tackles the problem of classifying data into distinct groups based on their features. An ideal clustering algorithm maximizes feature similarities within a cluster while minimizing the feature similarities across clusters. Some of the most common clustering algorithms include spectral clustering and k-means clustering. This project essential...

Machine learning and data mining methods are applied to perform large data analysis. Clustering methods are applied to group the related data values. Partitional clustering and hierarchical clustering methods are applied to handle the clustering operations. Tabular format data processing is carried out under the partitional clustering models. Tree based data clustering is adapted in the hierarc...

In clustering, global feature selection algorithms attempt to select a common feature subset that is relevant to all clusters. Consequently, they are not able to identify individual clusters that exist in different feature subspaces. In this paper, we propose a localized feature selection algorithm for clustering. The proposed algorithm computes adjusted and normalized scatter separability for ...