Data Mining - Concepts and Techniques.

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Many real-world data-mining applications involve obtaining predictive models using datasets with strongly imbalanced distributions of the target variable. Frequently, the least-common values of this target variable are associated with events that are highly relevant for end users (e.g., fraud detection, unusual returns on stock markets, anticipation of catastrophes, etc.). Moreover, the events may have different costs and benefits, which, when associated with the rarity of some of them on the available training data, creates serious problems to predictive modeling techniques. This article presents a survey of existing techniques for handling these important applications of predictive analytics. Although most of the existing work addresses classification tasks (nominal target variables), we also describe methods designed to handle similar problems within regression tasks (numeric target variables). In this survey, we discuss the main challenges raised by imbalanced domains, propose a definition of the problem, describe the main approaches to these tasks, propose a taxonomy of the methods, summarize the conclusions of existing comparative studies as well as some theoretical analyses of some methods, and refer to some related problems within predictive modeling.

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A Survey of Predictive Modeling on Imbalanced Domains

We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.

Density-based clustering based on hierarchical density estimates

In this paper, a novel text clustering method, improved krill herd algorithm with a hybrid function, called MMKHA, is proposed as an efficient clustering way to obtain promising and precise results in this domain. Krill herd is a new swarm-based optimization algorithm that imitates the behavior of a group of live krill. The potential of this algorithm is high because it performs better than other optimization methods; it balances the process of exploration and exploitation by complementing the strength of local nearby searching and global wide-range searching. Text clustering is the process of grouping significant amounts of text documents into coherent clusters in which documents in the same cluster are relevant. For the purpose of the experiments, six versions are thoroughly investigated to determine the best version for solving the text clustering. Eight benchmark text datasets are used for the evaluation process available at the Laboratory of Computational Intelligence (LABIC). Seven evaluation measures are utilized to validate the proposed algorithms, namely, ASDC, accuracy, precision, recall, F-measure, purity, and entropy. The proposed algorithms are compared with the other successful algorithms published in the literature. The results proved that the proposed improved krill herd algorithm with hybrid function achieved almost all the best results for all datasets in comparison with the other comparative algorithms.

Hybrid clustering analysis using improved krill herd algorithm

Sentiment analysis is an application of natural language processing. It is also known as emotion extraction or opinion mining. This is a very popular field of research in text mining. The basic idea is to find the polarity of the text and classify it into positive, negative or neutral. It helps in human decision making. To perform sentiment analysis, one has to perform various tasks like subjectivity detection, sentiment classification, aspect term extraction, feature extraction etc. This paper presents the survey of main approaches used for sentiment classification.

A survey of sentiment analysis techniques

A comprehensive survey of service function chain provisioning approaches in SDN and NFV architecture

Clustering, an extremely important technique in Data Mining is an automatic learning technique aimed at grouping a set of objects into subsets or clusters. The goal is to create clusters that are coherent internally, but substantially different from each other. Text Document Clustering refers to the clustering of related text documents into groups based upon their content. It is a fundamental operation used in unsupervised document organization, text data mining, automatic topic extraction, and information retrieval. Fast and high-quality document clustering algorithms play an important role in effectively navigating, summarizing, and organizing information. The documents to be clustered can be web news articles, abstracts of research papers etc. This paper proposes two techniques for efficient document clustering involving the application of soft computing approach as an intelligent hybrid approach PSO algorithm. The proposed approach involves partitioning Fuzzy C-Means algorithm and K-Means algorithm each hybridized with Particle Swarm Optimization (PSO). The performance of these hybrid algorithms has been evaluated against traditional partitioning techniques (K-Means and Fuzzy C Means).

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Evaluation of text document clustering approach based on particle swarm optimization

Cloud computing has very attractive features like elastic, on demand and fully managed computer system resources and services. However, due to its distributed and dynamic nature as well as vulnerabilities in virtualization implementation, the cloud environment is prone to various cyber-attacks and security issues related to cloud model. Some of them are inability to access data coming to and from cloud service, theft and misuse of data hosted, no control over sensitive data access, advance threats like malware injection attack, wrapping attacks, virtual machine escape, distributed denial of service attack (DDoS) etc. DDoS is one of the notorious attack. Despite a number of available potential solutions for the detection of DDoS attacks, the increasing frequency and potency of recent attacks and the constantly evolving attack vectors, necessitate the development of improved detection approaches. This article proposes a novel architecture that combines a well posed stacked sparse AutoEncoder (AE) for feature learning with a Deep Neural Network (DNN) for classification of network traffic into benign traffic and DDoS attack traffic. AE and DNN are optimized for detection of DDoS attacks by tuning the parameters using appropriately designed techniques. The improvements suggested in this article lead to low reconstruction error, prevent exploding and vanishing gradients, and lead to smaller network which avoids overfitting. A comparative analysis of the proposed approach with ten state-of-the-art approaches using performance metrics-detection accuracy, precision, recall and F1-Score, has been conducted. Experiments have been performed on CICIDS2017 and NSL-KDD standard datasets for validation. Proposed approach outperforms existing approaches over the NSL-KDD dataset and yields competitive results over the CICIDS2017 dataset.

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Hyperband Tuned Deep Neural Network With Well Posed Stacked Sparse AutoEncoder for Detection of DDoS Attacks in Cloud

Sentiment Analysis(SA) is a combination of emotions, opinions and subjectivity of text. Today, social networking sites like Twitter are tremendously used in expressing the opinions about a particular entity in the form of tweets which are limited to 140 characters. Reviews and opinions play a very important role in understanding peoples satisfaction regarding a particular entity. Such opinions have high potential for knowledge discovery. The main target of SA is to find opinions from tweets, extract sentiments from them and then define their polarity, i.e, positive, negative or neutral. Most of the work in this domain has been done for English Language. In this paper, we discuss and propose sentiment analysis using Hindi language. We will discuss an unsupervised lexicon method for classification.

Veenu Mangat

Papers

A survey of sentiment analysis techniques

A comprehensive survey of service function chain provisioning approaches in SDN and NFV architecture

Evaluation of text document clustering approach based on particle swarm optimization

Hyperband Tuned Deep Neural Network With Well Posed Stacked Sparse AutoEncoder for Detection of DDoS Attacks in Cloud

A practical approach to Sentiment Analysis of hindi tweets