B. S. Harish

A picture fuzzy clustering approach for brain tumor segmentation

This paper presents a Picture Fuzzy Clustering (PFC) method for MRI brain image segmentation. The PFC is based on the Picture fuzzy set, which is the generalization of the traditional fuzzy set and intuitionistic fuzzy set. In traditional fuzzy set, the problem of uncertainty arises in defining the membership function. Intuitionistic fuzzy set handles this uncertainty by considering hesitation degree. However, intuitionistic fuzzy set fails to solve real time problems which require answers like yes, abstain, no and refusal. The picture fuzzy set solves these problems by considering refusal degree along with membership, neutral and nonmembership degree. Thus, the cluster centers in the PFC may converge to a desirable location than the cluster centers obtained using traditional Fuzzy C-Means (FCM) and Intuitionistic Fuzzy Clustering (IFC). Experimentation is carried out on the standard MRI brain image dataset. To assess the performance, the proposed method is compared with the existing FCM and IFC methods. Results show that the proposed method gives the better result.

A Modified Intuitionistic Fuzzy Clustering Algorithm for Medical Image Segmentation

Abstract This paper presents a modified intuitionistic fuzzy clustering (IFCM) algorithm for medical image segmentation. IFCM is a variant of the conventional fuzzy C-means (FCM) based on intuitionistic fuzzy set (IFS) theory. Unlike FCM, IFCM considers both membership and nonmembership values. The existing IFCM method uses Sugeno’s and Yager’s IFS generators to compute nonmembership value. But for certain parameters, IFS constructed using above complement generators does not satisfy the elementary condition of intuitionism. To overcome this problem, this paper adopts a new IFS generator. Further, Hausdorff distance is used as distance metric to calculate the distance between cluster center and pixel. Extensive experimentations are carried out on standard datasets like brain, lungs, liver and breast images. This paper compares the proposed method with other IFS based methods. The proposed algorithm satisfies the elementary condition of intuitionism. Further, this algorithm outperforms other methods with the use of various cluster validity functions.

A modified Support Vector Clustering method for document categorization

In this paper, we propose a novel text categorization method based on modified Support Vector Clustering (SVC). SVC is a density based clustering approach, which handles the arbitrary shape clusters effectively. The main drawback of traditional SVC is that it treats unclassified documents as outliers. To overcome this problem, we employed Fuzzy C-Means (FCM) to cluster unclassified documents. The modified SVC (SVC-FCM) is applied to categorize text documents. The proposed method consists of three steps: In the first step, Regularized Locality Preserving Indexing (RLPI) is applied on Term Document Matrix (TDM) to reduce dimensionality of features. In second step, we use SVC to find base-cluster centers of documents. Finally, we use FCM to cluster unclassified documents. To evaluate the performance of the proposed method, we conducted experiments on standard 20-NewsGroup dataset.

Symbolic Representation of Text Documents Using Multiple Kernel FCM

In this paper, we proposed a novel method of representing text documents based on clustering of term frequency vector. In order to cluster the term frequency vectors, we make use of Multiple Kernel Fuzzy C-Means MKFCM. After clustering, term frequency vector of each cluster are used to form a interval valued representation symbolic representation by the use of mean and standard deviation. Further, interval value features are stored in knowledge base as a representative of the cluster. To corroborate the efficacy of the proposed model, we conducted extensive experimentation on standard datset like Reuters-21578 and 20 Newsgroup. We have compared our classification accuracy achieved by the Symbolic classifier with the other existing Naive Bayes classifier, KNN classifier and SVM classifier. The experimental result reveals that the classification accuracy achieved by using symbolic classifier is better than other three classifiers.

Segmenting MRI brain images using evolutionary computation technique

Medical image segmentation is a fundamental preprocessing step in most systems that supports diagnosis or planning of surgical operations. The traditional Fuzzy c means clustering algorithm performs well in the absence of noise. Traditional FCM leads to its non robust result mainly due to 1. Not utilizing the spatial information in the image. 2. Use of Euclidean distance. These limitations can be addressed by using robust spatial kernel FCM (RSKFCM). RSKFCM consider the spatial information and uses Gaussian kernel function to calculate the distance between the center and data points. Though RSKFCM gives good result, the main drawback behind this method is the inability of generating global minima for the objective function. To improve the efficiency of RSKFCM method, in this paper we proposed the genetic algorithm based RSKFCM. By using the genetic algorithm, RSKFCM initializes the cluster centers and reaches the global minima of the objective function. Experimentation is carried out on the standard brain image dataset. Experimental result reveals that the proposed genetic algorithm based RSKFCM outperforms other FCM methods with the use of various cluster validity functions.

Adaptive Initialization of Cluster Centers using Ant Colony Optimization: Application to Medical Images

Segmentation is a fundamental preprocessing step in medical imaging for diagnosis and surgical operations planning. The popular Fuzzy C-Means clustering algorithm perform well in the absence of noise, but it is non robust to noise as it makes use of the Euclidean distance and does not exploit the spatial information of the image. These limitations can be addressed by using the Robust Spatial Kernel FCM (RSKFCM) algorithm that takes advantage of the spatial information and uses a Gaussian kernel function to calculate the distance between the center and data points. Though RSKFCM gives a good result, the main drawback of this method is the inability of obtaining good minima for the objective function as it happens for many other clustering algorithms. To improve the efficiency of RSKFCM method, in this paper, we proposed the Ant Colony Optimization algorithm based RSKFCM (ACORSKFCM). By using the Ant Colony Optimization, RSKFCM initializes the cluster centers and reaches good minima of the objective function. Experimental results carried out on the standard medical datasets like Brain, Lungs, Liver and Breast images. The results show that the proposed approach outperforms many other FCM variants.

Classifying text documents using unconventional representation

Classification of text documents is one of the most common themes in the field of machine learning. Although a text document expresses a wide range of information, but it lacks the imposed structure of tradition database. Thus, unstructured data, particularly free running text data has to be transferred into a structured data. Hence, in this paper we represent the text document unconventionally by making use of symbolic data analysis concepts. We propose a new method of representing documents based on clustering of term frequency vectors. Term frequency vectors of each cluster are used to form a symbolic representation by the use of Mean and Standard Deviation. Further, term frequency vectors are used in the form a interval valued features. To cluster the term frequency vectors, we make use of Single Linkage, Complete Linkage, Average Linkage, K-Means and Fuzzy C-Means clustering algorithms. To corroborate the efficacy of the proposed model we conducted extensive experimentations on standard datasets like 20 Newsgroup Large, 20 Mini Newsgroup, Vehicles Wikipedia datasets and our own created datasets like Google Newsgroup and Research Article Abstracts. Experimental results reveal that the proposed model gives better results when compared to the state of the art techniques. In addition, as the method is based on a simple matching scheme, it requires a negligible time.

Classification of sentiments in short-text: an approach using mSMTP measure

Sentiment analysis or opinion mining is an automated process to recognize opinion, moods, emotions, attitude of individuals or communities through natural language processing, text analysis, and computational linguistics. In recent years, many studies concentrated on numerous blogs, tweets, forums and consumer review websites to identify sentiment of the communities. The information retrieved from social networking site will be in short informal text because of limited characters in blogging site or consumer review websites. Sentiment analysis in short-text is a challenging task, due to limitation of characters, user tends to shorten his/her conversation, which leads to misspellings, slang terms and shortened forms of words. Moreover, short-texts consists of more number of presence and absence of term/feature compared to regular text. In this work, our major goal is to classify sentiments into positive, negative or neutral polarity using new similarity measure. The proposed method embeds modified Similarity Measure for Text Processing (mSMTP) with K-Nearest Neighbor (KNN) classifier. The effectiveness of the proposed method is evaluated by comparing with Euclidean Distance, Cosine Similarity, Jaccard Coefficient and Correlation Coefficient. The proposed method is also compared with other classifiers like Support Vector Machine and Random Forest using benchmark dataset. The classification results are evaluated based on Accuracy, Precision, Recall and F-measure.

Classification of ECG Arrhythmia using symbolic dynamics through fuzzy clustering neural network

This paper presents automatic ECG arrhythmia classification method using symbolic dynamics through hybrid classifier. The proposed method consists of four steps: pre-processing, data extraction, symbolic time series construction and classification. In the proposed method, initially ECG signals are pre-processed to remove noise. Further, QRS complex is extracted followed by R peak detection. From R peak value, symbolic time series representation is formed. Finally, the symbolic time series is classified using Fuzzy clustering Neural Network (FCNN). To evaluate the proposed method we conducted the experiments on MIT-BIH dataset and compared the results with Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFNN) classifiers. The experimental results reveal that the FCNN classifier outperforms other two classifiers.

Clustering Text Documents Using Kernel Possibilistic C-Means

Text Document Clustering is one of the classic topics in text mining, which groups text documents in unsupervised way. There are various clustering techniques available to cluster text documents. Fuzzy C-Means (FCM) is one of the popular fuzzy-clustering algorithm. Unfortunately, Fuzzy C-Means algorithm is too sensitive to noise. Possibilistic C-Means overcomes this drawback by releasing the probabilistic constraint of the membership function. In this paper, we proposed a Kernel Possibilistic C-Means (KPCM) method for Text Document Clustering. Unlike the classical Possibilistic C-Means algorithm, the proposed method employs the kernel distance metric to calculate the distance between the cluster center and text document. We used standard 20NewsGroups dataset for experimentation and conducted comparison between proposed method (KPCM), Fuzzy C-Means, Kernel Fuzzy C-Means and Possibilistic C-Means. The experimental results reveal that the Kernel Possibilistic C-Means outperforms the other methods in terms of accuracy.