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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...
The concept of cloud computing is becoming prominent now a days. The phenomenon of cloud computing comprises the rationing of data and resources over an accessible network. Cloud comprises a large network of nodes, which provides services to the mobile nodes and the application which falls back on to the cloud are known as the cloud applications. In the previous researches, migration of the task to...
Medical data in the form of electronic patient records has grown manifold over the past few years. This data contains a plethora of hidden information which could prove extremely useful to the medical practitioner and contribute to the advancement of medical science in general. This chapter focuses on techniques that can be used to reveal interesting information in the form of associations between...
The process of predicting a qualitative response for an observation is referred to as classification. Supervised learning tools require labelled datasets to build classification models. However there are often instances when we have an unstructured dataset that doesn't have the output sequence for the corresponding input sequence, i.e. the dataset available is unlabeled. In such cases we need to use...
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...
Currently, huge sizes of indeterminate data are effortlessly collected or created at a very high pace in numerous real-life applications. Classifying this indefinite big data, is computationally intensive as large amount of data is related with existential probability of undefined or undetermined values of raw data. In this study, we propose a data mining approach for the classification of big dataset...
"Big Data" is a term that have jumped overnight from its roots. It can be described as an innovative technique and technology to save, distribute, manage, visualize and analyze larger-sized data with extreme velocity and methods to manage unstructured and structure incapable amount of data. Big data has high capacity to predict conclusion, with low cost consumption, increase efficiency and...
The ongoing increase in the usage of web has led to accumulation of large amounts of data every second. This has in turn made the research industry to grow and focus towards employing web usage mining for increasing the revenues for businesses, carrying out analysis on browsing behavior of web users, improving website layout and much more. Web usage mining is becoming increasingly popular due to the...
Association rule mining is a data mining method that extracts correlations, frequent patterns and causal dependencies between attributes of datasets. Association rules can be used for data classification as they provide a comprehensible and intuitive way to categorize data. The effectiveness of a rule is typically measured using support-confidence framework. This paper studies several other objective...
The rapid development of the internet and web publishing techniques create numerous information sources published as HTML pages on World Wide Web. However, there is lot of redundant and irrelevant information also on web pages. Navigation panels, Table of content (TOC), advertisements, copyright statements, service catalogs, privacy policies etc. on web pages are considered as relevant and irrelevant...
This paper discusses the novel area of Brain Informatics (BI). BI is an interdisciplinary field that studies Information processing and Neuroscience. First section of the paper discusses this area and identifies major research issues associated with BI. Second section provides a comprehensive literature survey of neuroimaging techniques and their pros and cons. It then relates the most promising technique...
This paper focuses on personalization of "user information needs" by applying clustering techniques of data mining. In the current data centric world, it is very important to analyze data properly and draw the crux from the available for effective creation and maintenance of user profiles. This paper throws explains the use of an indispensable technique called Clustering Technique, which...
Early diagnosis of any disease with less cost is always preferable. Diabetes is one such disease. It has become the fourth leading cause of death in developed countries and is also reaching epidemic proportions in many developing and newly industrialized nations. In this study, we investigate an automatic approach to diagnose Diabetes disease based on Bacterial Foraging Optimization and Artificial...
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...
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