Data Mining - Concepts and Techniques.

Textual information mining deals with various information extraction methods that can be evolved from the rapid growth of textual information through human machine interface for analyzing emotions which are taken by a facial expression. The problem of emotions in text is concerned with the fast development of web 2.0 documents that are assigned by users with emotion labels, namely: sadness, surprise, happiness, empathy, anger, warmness, boredom, and amusement. Such emotions can give a new characteristic for document categorization. Textual information mining deals with various information extraction methods that can evolved from the rapid growth of textual information through a human machine interface for analyzing emotions, which are taken by a facial expression. The problem of emotions from text is concerned with the fast development of web 2.0 documents that are assigned by users with emotion labels. Such emotions can give a new characteristic for document categorization.

Automatic Human Emotion Classification in Web Document Using Fuzzy Inference System (FIS): Human Emotion Classification

Automatic text categorization is the operation of sorting out the text documents into pre-defined text categories using some machine learning algorithms. Normally, it defines the most important approaches to organizing and making the use of a large volume of information exists in unstructured form. Nowadays, text categorization is becoming an extensively researched field of text mining and processing of languages. Word sense, semantic relationships among terms, text documents and categories are quite essential in order of enhancing the performances of categorization. Various surveys on text categorization have already been available which involve techniques of various text representation schemes to such extent but do not include several approaches that have been explored in text categorization over the standard techniques. Here, an exhaustive analysis of different text categorization approaches over the conventional approaches has been undertaken. This survey paper explores a wide variety of algorithms used for categorizing text documents and tries to assemble the existing works into three basic fields: conventional methods, fuzzy logic-based methods, deep learning-based methods. Further, conventional methods have been categorized into three fields: text categorization using handcrafted features, text categorization using nature-inspired algorithms and text categorization using graph-based methods. Furthermore, this survey provides a clear idea about the available libraries used for different algorithms, availability of datasets, categorization technologies explored in various non-Indian and Indian languages as well.

Text categorization: past and present

Abstract Personal data garnered from various sensors are often offloaded by applications to the cloud for analytics. This leads to a potential risk of disclosing private user information. We observe that the analytics run on the cloud are often limited to a machine learning model such as predicting a user’s activity using an activity classifier. We present Olympus, a privacy framework that limits the risk of disclosing private user information by obfuscating sensor data while minimally affecting the functionality the data are intended for. Olympus achieves privacy by designing a utility aware obfuscation mechanism, where privacy and utility requirements are modeled as adversarial networks. By rigorous and comprehensive evaluation on a real world app and on benchmark datasets, we show that Olympus successfully limits the disclosure of private information without significantly affecting functionality of the application.

https://www.sciendo.com/pdf/10.2478/popets-2019-0002

Olympus: Sensor Privacy through Utility Aware Obfuscation

Classification of documents is measured in terms of accuracy by comparing the actual labels with predicted labels for the classes. There are many machine learning techniques, which can be used to build a classifier, it is almost difficult to manually predict which technique should be used for classification, especially when we are working on Indic languages and there is no reliable method, which can give good results. Such area still need to be explored by processing documents with natural language processing, then applying machine learning techniques to build the classifier. This research article has collected 154 Hindi Poetries from web; processed it using NLP techniques, extracted features to build classifier with 5 Machine Learning methods namely support vector machine, naive bayes, decision tree algorithm, random forest and k-nearestneighbors. The results were examined in accuracy with holdout accuracy estimator and k-fold cross validation accuracy estimator to check the reliability of the methods. The results of this research will help to select the best document classifier for Hindi Poetries and increase accuracy by working on various feature extraction techniques and NLP techniques. The result of the experiment shows that the results of SVM, NB and random forest methods are better as compared to DTT and K-NN for used data set available in this experiment.

Data Classification with k-fold Cross Validation and Holdout Accuracy Estimation Methods with 5 Different Machine Learning Techniques

An efficient Devanagari character classification in printed and handwritten documents using SVM

Text Classification is a challenging and a red hot field in the current scenario and has great importance in text categorization applications. A lot of research work has been done in this field but there is a need to categorize a collection of text documents into mutually exclusive categories by extracting the concepts or features using supervised learning paradigm and different classification algorithms. In this paper, a new Fuzzy Similarity Based Concept Mining Model (FSCMM) is proposed to classify a set of text documents into pre - defined Category Groups (CG) by providing them training and preparing on the sentence, document and integrated corpora levels along with feature reduction, ambiguity removal on each level to achieve high system performance. Fuzzy Feature Category Similarity Analyzer (FFCSA) is used to analyze each extracted feature of Integrated Corpora Feature Vector (ICFV) with the corresponding categories or classes. This model uses Support Vector Machine Classifier (SVMC) to classify correctly the training data patterns into two groups; i. e., + 1 and – 1, thereby producing accurate and correct results. The proposed model works efficiently and effectively with great performance and high - accuracy results.

/pdf/a-fuzzy-similarity-based-concept-mining-model-for-text-2gabwshn7s.pdf

A Fuzzy Similarity Based Concept Mining Model for Text Classification

In the current era, there is a high demand of accurate text identification and categorization methods in N - Lingual non-scanned and scanned machine printed documents, where N represents mono, bi, tri or multi mode. In this paper, a technical study and analysis is presented to show N-lingual document classification for normal text, printed and handwritten documents. Text classification for normal text documents is simple, whereas in scanned machine printed systems, it inherently begins with the correct recognition of text, i.e.; characters and words. The steps involved in the latter case are script identification, page layout determination, separation of text and non-text data, line segmentation, word detection and finally character recognition. After performing such processing steps, text or script is identified and separated. Three statistically analyzed charts are also shown, which are based on content type classification, language-mode pair and most-to-least preferred languages of existing algorithms.

A technical study and analysis of text classification techniques in N - Lingual documents

In recent years, many information retrieval, character recognition, and feature extraction methodologies in Devanagari and especially in Hindi have been proposed for different domain areas. Due to enormous scanned data availability and to provide an advanced improvement of existing Hindi automated systems beyond optical character recognition, a new idea of Hindi printed and handwritten document classification system using support vector machine and fuzzy logic is introduced. This first pre-processes and then classifies textual imaged documents into predefined categories. With this concept, this article depicts a feasibility study of such systems with the relevance of Hindi, a survey report of statistical measurements of Hindi keywords obtained from different sources, and the inherent challenges found in printed and handwritten documents. The technical reviews are provided and graphically represented to compare many parameters and estimate contents, forms and classifiers used in various existing techniques.

Hindi Text Document Classification System Using SVM and Fuzzy: A Survey

In this new and current era of technology, advancements and techniques, efficient and effective text document classification is becoming a challenging and highly required area to capably categorize text documents into mutually exclusive categories. Fuzzy similarity provides a way to find the similarity of features among various documents. In this paper, a technical review on various fuzzy similarity based models is given. These models are discussed and compared to frame out their use and necessity. A tour of different methodologies is provided which is based upon fuzzy similarity related concerns. It shows that how text and web documents are categorized efficiently into different categories. Various experimental results of these models are also discussed. The technical comparisons among each model's parameters are shown in the form of a 3-D chart. Such study and technical review provide a strong base of research work done on fuzzy similarity based text document categorization.

/pdf/a-technical-study-and-analysis-on-fuzzy-similarity-based-1k7y75p0hg.pdf

Shalini Puri

Papers

An efficient Devanagari character classification in printed and handwritten documents using SVM

A Fuzzy Similarity Based Concept Mining Model for Text Classification

A technical study and analysis of text classification techniques in N - Lingual documents

Hindi Text Document Classification System Using SVM and Fuzzy: A Survey

A technical study and analysis on fuzzy similarity based models for text classification