Rashmi Dutta Baruah

Evolving local means method for clustering of streaming data

A new on-line evolving clustering approach for streaming data is proposed in this paper. The approach is based on the concept that local mean of samples within a region has the highest density and the gradient of the density points towards the local mean. The algorithm merely requires recursive calculation of local mean and variance, due to which it easily meets the memory and time constraints for data stream processing. The experimental results using synthetic and benchmark datasets show that the proposed approach attains results at par with offline approach and is comparable to popular density-based mean-shift clustering yet it is significantly more efficient being one-pass and non-iterative.

DEC: Dynamically Evolving Clustering and Its Application to Structure Identification of Evolving Fuzzy Models

Identification of models from input-output data essentially requires estimation of appropriate cluster centers. In this paper, a new online evolving clustering approach for streaming data is proposed. Unlike other approaches that consider either the data density or distance from existing cluster centers, this approach uses cluster weight and distance before generating new clusters. To capture the dynamics of the data stream, the cluster weight is defined in both data and time space in such a way that it decays exponentially with time. It also applies concepts from computational geometry to determine the neighborhood information while forming clusters. A distinction is made between core and noncore clusters to effectively identify the real outliers. The approach efficiently estimates cluster centers upon which evolving Takagi-Sugeno models are developed. The experimental results with developed models show that the proposed approach attains results at par or better than existing approaches and significantly reduces the computational overhead, which makes it suitable for real-time applications.

Simpl_eClass: Simplified potential-free evolving fuzzy rule-based classifiers

This paper presents the sequel of evolving fuzzy rule-based classifier eClass, called here as simplified evolving classifier, simpl_eClass. Similarly to eClass, simpl_eClass comprises of two different classifiers, namely zero and first order (simpl_eClass0 and simpl_eClass1). The two classifiers differ from each other in terms of the consequent part of the fuzzy rules, and the classification strategy used. The design of simpl_eClass is based on the density increment principle introduced recently in so called simpl_eTS+ approach. The rule learning in simpl_eClass does not involve computation of potential values that allows it to attain computationally much less expensive model update phase compared to eClass. As compared to other FRB classifiers, it retains all the advantages of eClass, such as being on-line and evolving, having zero and first order. In comparison with other non-fuzzy classifiers it has the advantage of interpretability and transparency (especially zero order type). The goals of this paper are to demonstrate the applicability of simpl_eTS+ to classification task, and to empirically show that the simplification of eClass to simpl_eClass by using potential-free approach does not compromise the accuracy of the classifiers. In order to attain the goals, the classifiers are tested by performing several experiments using benchmark data sets. The simpl_eClass1 classifier is also applied to the real-life problem of on-line scene categorization for low-resource devices benefiting from its low computational cost. The results obtained from the experiments endorse that simpl_eClass achieves the accuracy of eClass while simplifying rule learning process.

Evolving fuzzy systems for data streams: a survey

Evolving fuzzy systems (EFSs) can be regarded as intelligent systems based on fuzzy rule‐based or neuro‐fuzzy models with the ability to learn continuously and to gradually develop with the objective of enhancing their performance. Such systems learn in online mode by analyzing incoming samples, and adjusting both structure and parameters. The objective of this chapter is to present a brief overview of some early as well as recent EFSs by focusing on their architecture, design algorithms along with the merits and demerits, and various applications.

Real time recognition of human activities from wearable sensors by evolving classifiers

A new approach to real-time human activity recognition (HAR) using evolving self-learning fuzzy rule-based classifier (eClass) will be described in this paper. A recursive version of the principle component analysis (PCA) and linear discriminant analysis (LDA) pre-processing methods is coupled with the eClass leading to a new approach for HAR which does not require computation and time consuming pre-training and data from many subjects. The proposed new method for evolving HAR (eHAR) takes into account the specifics of each user and possible evolution in time of her/his habits. Data streams from several wearable devices which make possible to develop a pervasive intelligence enabling them to personalize/tune to the specific user were used for the experimental part of the paper.

Dynamically evolving clustering for data streams

In this paper, a new online evolving clustering approach for streaming data is proposed, named Dynamically Evolving Clustering method. The clustering approach attempts to meet the following three key requirements of data stream clustering: (i) fast and memory efficient (ii) adaptive (iii) robust to noise. The proposed clustering approach processes one sample at a time and makes necessary changes to the model and then forgets the processed sample. This feature naturally makes it adaptive to changes in the data pattern. The clustering method considers both distance and weight before generating new clusters. This avoids generation of large number of clusters. Further, to capture the dynamics of the data stream, the weight uses an exponential decay model. Since in data streaming environment, a low density cluster can be outlier points or seed of actual cluster, DEC applies a strategy that enables detecting and removing only those low density clusters that are real outliers. To evaluate the performance of the proposed clustering approach, experiments were conducted using benchmark dataset. The results show that the Dynamically Evolving Clustering approach can separate the data well which are evolving in nature.

Evolving social network analysis: A case study on mobile phone data

Mobile phone data can provide rich information on human activities and their social relationships which are dynamic in nature. Analysis of such social networks emerging from phone calls of mobile users can be useful in many aspects. In this paper we report the methods and results from a case study on the analysis of a social network from mobile phone data. The analysis involves tracking the dynamics of the network, identifying key individuals and their close associates, and identifying individuals having communication pattern similar to the key individuals. We introduce novel measures to quantify, the evolution in the network, significance of an individual, and social association of an individual. In order to group individuals having similar communication pattern, we applied recently proposed online clustering approach called eClustering (evolving clustering) due to its adaptive nature and low computational overhead. The results show the pertinence of the proposed quantification measures to analysis of evolving social network.

Dynamically evolving fuzzy classifier for real-time classification of data streams

In this paper, a novel evolving fuzzy rule-based classifier is presented. The proposed classifier addresses the three fundamental issues of data stream learning, viz., computational efficiency in terms of processing time and memory requirements, adaptive to changes, and robustness to noise. Though, there are several online classifiers available, most of them do not take into account all the three issues simultaneously. The newly proposed classifier is inherently adaptive and can attend to any minute changes as it learns the rules in online manner by considering each incoming example. However, it should be emphasized that it can easily distinguish noise from new concepts and automatically handles noise. The performance of the classifier is evaluated using real-life data with evolving characteristic and compared with state-of-the-art adaptive classifiers. The experimental results show that the classifier attains a simple model in terms of number of rules. Further, the memory requirements and processing time per sample does not increase linearly with the progress of the stream. Thus, the classifier is capable of performing both prediction and model update in real-time in a streaming environment.

Online learning and prediction of data streams using dynamically evolving fuzzy approach

Learning and prediction in a data streaming environment is challenging due to continuous arrival of enormous data in high speed that often evolves with time. In this paper we present a dynamically evolving fuzzy rule-based model that predicts and learns from each instance in the stream, taking into account the principal issues of streaming environment viz., limited memory, real time, and dynamic nature. The fuzzy model essentially uses a newly proposed dynamically evolving clustering method for learning the structure. Unlike other approaches that consider either the data density or distance from existing cluster centres, this approach considers both density and distance to decide if a new cluster is to be generated. To capture the dynamics of the data stream, the density is defined in both data and time space in such a way that it decays exponentially with time. A distinction is made between core and non-core clusters to effectively identify the real outliers. The experimental results using benchmark and real datasets show that the proposed approach attains results at par or better than existing approaches and significantly reduces the computational overhead.

Acoustic sensor based activity recognition using ensemble of one-class classifiers

In this paper we address the problem of human activity recognition based only on acoustic modality. The ultimate goal is continuous acoustic monitoring of public places like parks and bus stops for detecting littering activities so that the people involved in such acts can be prompted to bin appropriately. We exploit the fact that when human interacts with objects, a characteristic sound is produced, and this sound can be used to recognize the activity. We propose a method based on perceptual features and ensemble of fuzzy rule-based one-class classifiers for activity recognition. The method is validated using real data and compared with support vector machine classifier. The results show that the classifier has very low false alarm rate and potentially well suited for incremental learning.