This document discusses document classification using a k-nearest neighbors algorithm with dynamic attribute weighting and bootstrap sampling. It begins with an introduction to text mining and document classification. It then describes k-nearest neighbors classification and how bootstrap sampling can be used to improve k-NN by assigning different weightings to attributes. The document evaluates this approach and compares its performance to traditional k-NN classification.
Survey on Existing Text Mining Frameworks and A Proposed Idealistic Framework...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
ESTIMATION OF REGRESSION COEFFICIENTS USING GEOMETRIC MEAN OF SQUARED ERROR F...ijaia
Regression models and their statistical analyses is one of the most important tool used by scientists and practitioners. The aim of a regression model is to fit parametric functions to data. It is known that the true regression is unknown and specific methods are created and used strictly pertaining to the roblem. For the pioneering work to develop procedures for fitting functions, we refer to the work on the methods of least
absolute deviations, least squares deviations and minimax absolute deviations. Today’s widely celebrated
procedure of the method of least squares for function fitting is credited to the published works of Legendre and Gauss. However, the least squares based models in practice may fail to provide optimal results in nonGaussian situations especially when the errors follow distributions with the fat tails. In this paper an unorthodox method of estimating linear regression coefficients by minimising GMSE(geometric mean of squared errors) is explored. Though GMSE(geometric mean of squared errors) is used to compare models it is rarely used to obtain the coefficients. Such a method is tedious to handle due to the large number of roots obtained by minimisation of the loss function. This paper offers a way to tackle that problem.
Application is illustrated with the ‘Advertising’ dataset from ISLR and the obtained results are compared
with the results of the method of least squares for single index linear regression model.
Survey on Existing Text Mining Frameworks and A Proposed Idealistic Framework...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
ESTIMATION OF REGRESSION COEFFICIENTS USING GEOMETRIC MEAN OF SQUARED ERROR F...ijaia
Regression models and their statistical analyses is one of the most important tool used by scientists and practitioners. The aim of a regression model is to fit parametric functions to data. It is known that the true regression is unknown and specific methods are created and used strictly pertaining to the roblem. For the pioneering work to develop procedures for fitting functions, we refer to the work on the methods of least
absolute deviations, least squares deviations and minimax absolute deviations. Today’s widely celebrated
procedure of the method of least squares for function fitting is credited to the published works of Legendre and Gauss. However, the least squares based models in practice may fail to provide optimal results in nonGaussian situations especially when the errors follow distributions with the fat tails. In this paper an unorthodox method of estimating linear regression coefficients by minimising GMSE(geometric mean of squared errors) is explored. Though GMSE(geometric mean of squared errors) is used to compare models it is rarely used to obtain the coefficients. Such a method is tedious to handle due to the large number of roots obtained by minimisation of the loss function. This paper offers a way to tackle that problem.
Application is illustrated with the ‘Advertising’ dataset from ISLR and the obtained results are compared
with the results of the method of least squares for single index linear regression model.
Data mining , knowledge discovery is the process
of analyzing data from different perspectives and summarizing it
into useful information - information that can be used to increase
revenue, cuts costs, or both. Data mining software is one of a
number of analytical tools for analyzing data. It allows users to
analyze data from many different dimensions or angles, categorize
it, and summarize the relationships identified. Technically, data
mining is the process of finding correlations or patterns among
dozens of fields in large relational databases. The goal of
clustering is to determine the intrinsic grouping in a set of
unlabeled data. But how to decide what constitutes a good
clustering? It can be shown that there is no absolute “best”
criterion which would be independent of the final aim of the
clustering. Consequently, it is the user which must supply this
criterion, in such a way that the result of the clustering will suit
their needs.
For instance, we could be interested in finding
representatives for homogeneous groups (data reduction), in
finding “natural clusters” and describe their unknown properties
(“natural” data types), in finding useful and suitable groupings
(“useful” data classes) or in finding unusual data objects (outlier
detection).Of late, clustering techniques have been applied in the
areas which involve browsing the gathered data or in categorizing
the outcome provided by the search engines for the reply to the
query raised by the users. In this paper, we are providing a
comprehensive survey over the document clustering.
Sentimental classification analysis of polarity multi-view textual data using...IJECEIAES
The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats. Besides, in the case of the textual data exists as multi-view or unstructured data formats, the hybrid and integrated analysis efforts of text data mining algorithms are vital to get helpful results. The objective of this research is to enhance the knowledge discovery from sentimental multi-view textual data which can be considered as unstructured data format to classify the polarity information documents in the form of two different categories or types of useful information. A proposed framework with integrated data mining algorithms has been discussed in this paper, which is achieved through the application of X-means algorithm for clustering and HotSpot algorithm of association rules. The analysis results have shown improved accuracies of classifying the sentimental multi-view textual data into two categories through the application of the proposed framework on online polarity user-reviews dataset upon a given topics.
A Text Mining Research Based on LDA Topic Modellingcsandit
A Large number of digital text information is gener
ated every day. Effectively searching,
managing and exploring the text data has become a m
ain task. In this paper, we first represent
an introduction to text mining and a probabilistic
topic model Latent Dirichlet allocation. Then
two experiments are proposed - Wikipedia articles a
nd users’ tweets topic modelling. The
former one builds up a document topic model, aiming
to a topic perspective solution on
searching, exploring and recommending articles. The
latter one sets up a user topic model,
providing a full research and analysis over Twitter
users’ interest. The experiment process
including data collecting, data pre-processing and
model training is fully documented and
commented. Further more, the conclusion and applica
tion of this paper could be a useful
computation tool for social and business research.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
The diversity and complexity of contents available on the web have dramatically increased in recent years. Multimedia content such as images, videos, maps, voice recordings has been published more often than before. Document genres have also been diversified, for instance, news, blogs, FAQs, wiki. These diversified information sources are often dealt with in a separated way. For example, in web search, users have to switch between search verticals to access different sources. Recently, there has been a growing interest in finding effective ways to aggregate these information sources so that to hide the complexity of the information spaces to users searching for relevant information. For example, so-called aggregated search investigated by the major search engine companies will provide search results from several sources in a single result page. Aggregation itself is not a new paradigm; for instance, aggregate operators are common in database technology.
This talk presents the challenges faced by the like of web search engines and digital libraries in providing the means to aggregate information from several and complex information spaces in a way that helps users in their information seeking tasks. It also discusses how other disciplines including databases, artificial intelligence, and cognitive science can be brought into building effective and efficient aggregated search systems.
A Document Exploring System on LDA Topic Model for Wikipedia Articlesijma
A Large number of digital text information is generated every day. Effectively searching, managing and
exploring the text data has become a main task. In this paper, we first present an introduction to text
mining and LDA topic model. Then we deeply explained how to apply LDA topic model to text corpus by
doing experiments on Simple Wikipedia documents. The experiments include all necessary steps of data
retrieving, pre-processing, fitting the model and an application of document exploring system. The result of
the experiments shows LDA topic model working effectively on documents clustering and finding the
similar documents. Furthermore, the document exploring system could be a useful research tool for
students and researchers.
Structured and Unstructured Information Extraction Using Text Mining and Natu...rahulmonikasharma
Information on web is increasing at infinitum. Thus, web has become an unstructured global area where information even if available, cannot be directly used for desired applications. One is often faced with an information overload and demands for some automated help. Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents by means of Text Mining and Natural Language Processing (NLP) techniques. Extracted structured information can be used for variety of enterprise or personal level task of varying complexity. The Information Extraction (IE) in also a set of knowledge in order to answer to user consultations using natural language. The system is based on a Fuzzy Logic engine, which takes advantage of its flexibility for managing sets of accumulated knowledge. These sets may be built in hierarchic levels by a tree structure. Information extraction is structured data or knowledge from unstructured text by identifying references to named entities as well as stated relationships between such entities. Data mining research assumes that the information to be “mined” is already in the form of a relational database. IE can serve an important technology for text mining. The knowledge discovered is expressed directly in the documents to be mined, then IE alone can serve as an effective approach to text mining. However, if the documents contain concrete data in unstructured form rather than abstract knowledge, it may be useful to first use IE to transform the unstructured data in the document corpus into a structured database, and then use traditional data mining tools to identify abstract patterns in this extracted data. We propose a novel method for text mining with natural language processing techniques to extract the information from data base with efficient way, where the extraction time and accuracy is measured and plotted with simulation. Where the attributes of entities and relationship entities from structured and semi structured information .Results are compared with conventional methods.
Automatic Text Classification using Supervised Learningdbpublications
As the time goes on and on,
digitization of text has been increasing remarkably and
the need to organize, categorize and classify text has
become indispensable. Disorganization and very little
categorization and classification of text may result in
gradual lower response time of text or information
retrieval. Therefore it is very important and necessary
to organize, categorize and classify texts and digitized
documents according to description proposed by text
mining experts and computer scientists. Automated
text classification has been considered as a imperative
method to manage and process a large amount of
documents in digital forms that are widespread and
continuously increasing. In general, text classification
plays and substantial role in information extraction
and text retrieval, and question answering. This paper
emphasizes the text classification process using
machine learning techniques.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Контроль операторами сотовой связи наличия согласий абонентов на получение SM...Devino Telecom
Devino Telecom решает любые маркетинговые задачи клиентов с помощью собственной многофункциональной платформы для рассылки SMS, email- и voice-сообщений.
Оператор предлагает сервисы обратной связи для пользователей мобильных устройств и сервисы мобильных платежей (m-commerce). Три SMS-центра Devino Telecom напрямую подключены к сетям операторов связи, что позволяет доставлять сообщения в 850 сетей в 220 странах мира. Компания основана в 2005 году.
http://www.devinotele.com/
Data mining , knowledge discovery is the process
of analyzing data from different perspectives and summarizing it
into useful information - information that can be used to increase
revenue, cuts costs, or both. Data mining software is one of a
number of analytical tools for analyzing data. It allows users to
analyze data from many different dimensions or angles, categorize
it, and summarize the relationships identified. Technically, data
mining is the process of finding correlations or patterns among
dozens of fields in large relational databases. The goal of
clustering is to determine the intrinsic grouping in a set of
unlabeled data. But how to decide what constitutes a good
clustering? It can be shown that there is no absolute “best”
criterion which would be independent of the final aim of the
clustering. Consequently, it is the user which must supply this
criterion, in such a way that the result of the clustering will suit
their needs.
For instance, we could be interested in finding
representatives for homogeneous groups (data reduction), in
finding “natural clusters” and describe their unknown properties
(“natural” data types), in finding useful and suitable groupings
(“useful” data classes) or in finding unusual data objects (outlier
detection).Of late, clustering techniques have been applied in the
areas which involve browsing the gathered data or in categorizing
the outcome provided by the search engines for the reply to the
query raised by the users. In this paper, we are providing a
comprehensive survey over the document clustering.
Sentimental classification analysis of polarity multi-view textual data using...IJECEIAES
The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats. Besides, in the case of the textual data exists as multi-view or unstructured data formats, the hybrid and integrated analysis efforts of text data mining algorithms are vital to get helpful results. The objective of this research is to enhance the knowledge discovery from sentimental multi-view textual data which can be considered as unstructured data format to classify the polarity information documents in the form of two different categories or types of useful information. A proposed framework with integrated data mining algorithms has been discussed in this paper, which is achieved through the application of X-means algorithm for clustering and HotSpot algorithm of association rules. The analysis results have shown improved accuracies of classifying the sentimental multi-view textual data into two categories through the application of the proposed framework on online polarity user-reviews dataset upon a given topics.
A Text Mining Research Based on LDA Topic Modellingcsandit
A Large number of digital text information is gener
ated every day. Effectively searching,
managing and exploring the text data has become a m
ain task. In this paper, we first represent
an introduction to text mining and a probabilistic
topic model Latent Dirichlet allocation. Then
two experiments are proposed - Wikipedia articles a
nd users’ tweets topic modelling. The
former one builds up a document topic model, aiming
to a topic perspective solution on
searching, exploring and recommending articles. The
latter one sets up a user topic model,
providing a full research and analysis over Twitter
users’ interest. The experiment process
including data collecting, data pre-processing and
model training is fully documented and
commented. Further more, the conclusion and applica
tion of this paper could be a useful
computation tool for social and business research.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
The diversity and complexity of contents available on the web have dramatically increased in recent years. Multimedia content such as images, videos, maps, voice recordings has been published more often than before. Document genres have also been diversified, for instance, news, blogs, FAQs, wiki. These diversified information sources are often dealt with in a separated way. For example, in web search, users have to switch between search verticals to access different sources. Recently, there has been a growing interest in finding effective ways to aggregate these information sources so that to hide the complexity of the information spaces to users searching for relevant information. For example, so-called aggregated search investigated by the major search engine companies will provide search results from several sources in a single result page. Aggregation itself is not a new paradigm; for instance, aggregate operators are common in database technology.
This talk presents the challenges faced by the like of web search engines and digital libraries in providing the means to aggregate information from several and complex information spaces in a way that helps users in their information seeking tasks. It also discusses how other disciplines including databases, artificial intelligence, and cognitive science can be brought into building effective and efficient aggregated search systems.
A Document Exploring System on LDA Topic Model for Wikipedia Articlesijma
A Large number of digital text information is generated every day. Effectively searching, managing and
exploring the text data has become a main task. In this paper, we first present an introduction to text
mining and LDA topic model. Then we deeply explained how to apply LDA topic model to text corpus by
doing experiments on Simple Wikipedia documents. The experiments include all necessary steps of data
retrieving, pre-processing, fitting the model and an application of document exploring system. The result of
the experiments shows LDA topic model working effectively on documents clustering and finding the
similar documents. Furthermore, the document exploring system could be a useful research tool for
students and researchers.
Structured and Unstructured Information Extraction Using Text Mining and Natu...rahulmonikasharma
Information on web is increasing at infinitum. Thus, web has become an unstructured global area where information even if available, cannot be directly used for desired applications. One is often faced with an information overload and demands for some automated help. Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents by means of Text Mining and Natural Language Processing (NLP) techniques. Extracted structured information can be used for variety of enterprise or personal level task of varying complexity. The Information Extraction (IE) in also a set of knowledge in order to answer to user consultations using natural language. The system is based on a Fuzzy Logic engine, which takes advantage of its flexibility for managing sets of accumulated knowledge. These sets may be built in hierarchic levels by a tree structure. Information extraction is structured data or knowledge from unstructured text by identifying references to named entities as well as stated relationships between such entities. Data mining research assumes that the information to be “mined” is already in the form of a relational database. IE can serve an important technology for text mining. The knowledge discovered is expressed directly in the documents to be mined, then IE alone can serve as an effective approach to text mining. However, if the documents contain concrete data in unstructured form rather than abstract knowledge, it may be useful to first use IE to transform the unstructured data in the document corpus into a structured database, and then use traditional data mining tools to identify abstract patterns in this extracted data. We propose a novel method for text mining with natural language processing techniques to extract the information from data base with efficient way, where the extraction time and accuracy is measured and plotted with simulation. Where the attributes of entities and relationship entities from structured and semi structured information .Results are compared with conventional methods.
Automatic Text Classification using Supervised Learningdbpublications
As the time goes on and on,
digitization of text has been increasing remarkably and
the need to organize, categorize and classify text has
become indispensable. Disorganization and very little
categorization and classification of text may result in
gradual lower response time of text or information
retrieval. Therefore it is very important and necessary
to organize, categorize and classify texts and digitized
documents according to description proposed by text
mining experts and computer scientists. Automated
text classification has been considered as a imperative
method to manage and process a large amount of
documents in digital forms that are widespread and
continuously increasing. In general, text classification
plays and substantial role in information extraction
and text retrieval, and question answering. This paper
emphasizes the text classification process using
machine learning techniques.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Контроль операторами сотовой связи наличия согласий абонентов на получение SM...Devino Telecom
Devino Telecom решает любые маркетинговые задачи клиентов с помощью собственной многофункциональной платформы для рассылки SMS, email- и voice-сообщений.
Оператор предлагает сервисы обратной связи для пользователей мобильных устройств и сервисы мобильных платежей (m-commerce). Три SMS-центра Devino Telecom напрямую подключены к сетям операторов связи, что позволяет доставлять сообщения в 850 сетей в 220 странах мира. Компания основана в 2005 году.
http://www.devinotele.com/
6ο Δημοτικό Σχολείο Πατρών Συνάντηση με τον συγγραφέα Μάνο ΚοντολέωνVasiliki Resvani
Συνάντηση με το συγγραφέα Μάνο Κοντολέων. Τα πρωτάκια συνάντησαν το συγγραφέα και του έδωσαν τις ζωγραφιές τους από τα βιβλία που είχαν διαβάσει. Δημιούργησαν μαζί μια ιστορία η οποία παρουσιάζεται εδώ.
"Analysis of Different Text Classification Algorithms: An Assessment "ijtsrd
Theoretical Classification of information has become a significant research region. The way toward ordering archives into predefined classifications dependent on their substance is Text characterization. It is the mechanized task of common language writings to predefined classifications. The essential prerequisite of content recovery frameworks is content characterization, which recover messages because of a client inquiry, and content getting frameworks, which change message here and there, for example, responding to questions, creating outlines or removing information. In this paper we are concentrating the different grouping calculations. Order is the way toward isolating the information to certain gatherings that can demonstration either conditionally or freely. Our fundamental point is to show the examination of the different characterization calculations like K nn, Na¯ve Bayes, Decision Tree, Random Forest and Support Vector Machine SVM with quick digger and discover which calculation will be generally reasonable for the clients. Adarsh Raushan | Prof. Ankur Taneja | Prof. Naveen Jain "Analysis of Different Text Classification Algorithms: An Assessment" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29869.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/29869/analysis-of-different-text-classification-algorithms-an-assessment/adarsh-raushan
Text mining has turned out to be one of the in vogue handle that has been joined in a few research
fields, for example, computational etymology, Information Retrieval (IR) and data mining. Natural
Language Processing (NLP) methods were utilized to extricate learning from the textual text that is
composed by people. Text mining peruses an unstructured form of data to give important
information designs in a most brief day and age. Long range interpersonal communication locales
are an awesome wellspring of correspondence as the vast majority of the general population in this
day and age utilize these destinations in their everyday lives to keep associated with each other. It
turns into a typical practice to not compose a sentence with remedy punctuation and spelling. This
training may prompt various types of ambiguities like lexical, syntactic, and semantic and because of
this kind of indistinct data; it is elusive out the genuine data arrange. As needs be, we are directing
an examination with the point of searching for various text mining techniques to get different
textual requests via web-based networking media sites. This review expects to depict how
contemplates in online networking have utilized text investigation and text mining methods to
identify the key topics in the data. This study concentrated on examining the text mining
contemplates identified with Facebook and Twitter; the two prevailing web-based social networking
on the planet. Aftereffects of this overview can fill in as the baselines for future text mining research.
Decision Support for E-Governance: A Text Mining ApproachIJMIT JOURNAL
Information and communication technology has the capability to improve the process by which governments involve citizens in formulating public policy and public projects. Even though much of government regulations may now be in digital form (and often available online), due to their complexity and diversity, identifying the ones relevant to a particular context is a non-trivial task. Similarly, with the advent of a number of electronic online forums, social networking sites and blogs, the opportunity of gathering citizens’ petitions and stakeholders’ views on government policy and proposals has increased greatly, but the volume and the complexity of analyzing unstructured data makes this difficult. On the other hand, text mining has come a long way from simple keyword search, and matured into a discipline capable of dealing with much more complex tasks. In this paper we discuss how text-mining techniques can help in retrieval of information and relationships from textual data sources, thereby assisting policy makers in discovering associations between policies and citizens’ opinions expressed in electronic public forums and blogs etc. We also present here, an integrated text mining based architecture for e-governance decision support along with a discussion on the Indian scenario.
Evaluating the efficiency of rule techniques for file classificationeSAT Journals
Abstract Text mining refers to the process of deriving high quality information from text. It is also known as knowledge discovery from text (KDT), deals with the machine supported analysis of text. It is used in various areas such as information retrieval, marketing, information extraction, natural language processing, document similarity, and so on. Document Similarity is one of the important techniques in text mining. In document similarity, the first and foremost step is to classify the files based on their category. In this research work, various classification rule techniques are used to classify the computer files based on their extensions. For example, the extension of computer files may be pdf, doc, ppt, xls, and so on. There are several algorithms for rule classifier such as decision table, JRip, Ridor, DTNB, NNge, PART, OneR and ZeroR. In this research work, three classification algorithms namely decision table, DTNB and OneR classifiers are used for performing classification of computer files based on their extension. The results produced by these algorithms are analyzed by using the performance factors classification accuracy and error rate. From the experimental results, DTNB proves to be more efficient than other two techniques. Index Terms: Data mining, Text mining, Classification, Decision table, DTNB, OneR
Text Mining is the technique that helps users to find out useful information from a large amount of text documents on the web or database. Most popular text mining and classification methods have adopted term-based approaches. The term based approaches and the pattern-based method describing user preferences. This review paper analyse how the text mining work on the three level i.e sentence level, document level and feature level. In this paper we review the related work which is previously done. This paper also demonstrated that what are the problems arise while doing text mining done at the feature level. This paper presents the technique to text mining for the compound sentences.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Text Mining is an Important part of data mining and it is used nowadays on a large scale. This mining technique is used to find patterns in text data collected from many online sources , and to gain some interestings insights from the patterns observed. Since text is basically everywhere on the internet, it becomes quite difficult to get the data in structured format, which is why text mining plays a huge role. It uses NLP(Natural Language Processing Techniques) to automate the text mining and this concept is used in Machine Learning.
Frequent Item set Mining of Big Data for Social MediaIJERA Editor
Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. Bigdata includes data from email, documents, pictures, audio, video files, and other sources that do not fit into a relational database. This unstructured data brings enormous challenges to Bigdata.The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. Therefore, big data implementations need to be analyzed and executed as accurately as possible. The proposed model structures the unstructured data from social media in a structured form so that data can be queried efficiently by using Hadoop MapReduce framework. The Bigdata mining is essential in order to extract value from massive amount of data. MapReduce is efficient method to deal with Big data than traditional techniques.The proposed Linguistic string matching Knuth-Morris-Pratt algorithm and K-Means clustering algorithm gives proper platform to extract value from massive amount of data and recommendation for user.Linguistic matching techniques such as Knuth–Morris–Pratt string matching algorithm are very useful in giving proper matching output to user query. The K-Means algorithm is one which works on clustering data using vector space model. It can be an appropriate method to produce recommendation for user.
Frequent Item set Mining of Big Data for Social MediaIJERA Editor
Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. Bigdata includes data from email, documents, pictures, audio, video files, and other sources that do not fit into a relational database. This unstructured data brings enormous challenges to Bigdata.The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. Therefore, big data implementations need to be analyzed and executed as accurately as possible. The proposed model structures the unstructured data from social media in a structured form so that data can be queried efficiently by using Hadoop MapReduce framework. The Bigdata mining is essential in order to extract value from massive amount of data. MapReduce is efficient method to deal with Big data than traditional techniques.The proposed Linguistic string matching Knuth-Morris-Pratt algorithm and K-Means clustering algorithm gives proper platform to extract value from massive amount of data and recommendation for user.Linguistic matching techniques such as Knuth–Morris–Pratt string matching algorithm are very useful in giving proper matching output to user query. The K-Means algorithm is one which works on clustering data using vector space model. It can be an appropriate method to produce recommendation for user
Survey of Machine Learning Techniques in Textual Document ClassificationIOSR Journals
Classification of Text Document points towards associating one or more predefined categories based
on the likelihood expressed by the training set of labeled documents. Many machine learning algorithms plays
an important role in training the system with predefined categories. The importance of Machine learning
approach has felt because of which the study has been taken up for text document classification based on the
statistical event models available. The aim of this paper is to present the important techniques and
methodologies that are employed for text documents classification, at the same time making awareness of some
of the interesting challenges that remain to be solved, focused mainly on text representation and machine
learning techniques.
Post 1What is text analytics How does it differ from text mini.docxstilliegeorgiana
Post 1:
What is text analytics? How does it differ from text mining?
Text Analytics is applying of statistical and machine learning techniques to be able to predict /prescribe or infer any information from the text-mined data. Text mining is a tool that helps in getting the data cleaned up.Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data.
Differences between Text Mining and Text Analytics:
• Text Mining and Text Analytics solve the same problems, but use different techniques and are complementary ways to automatically extract meaning from text.
• Text Analytics is developed within the field of computational linguistics. It has the ability to encode human understanding into a series of linguistic rules which are generated by humans are high in precision, but they do not automatically adapt and are usually fragile when tried in new situations.
• Text mining is a newer discipline arising out of the fields of statistics, data mining, and machine learning. Its strength is the ability to inductively create models from collections of historical data. Because statistical models are learned from training data they are adaptive and can identify “unknown unknowns”, leading to the better recall. Still, they can be prone to missing something that would seem obvious to a human.
• Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data.
• Due to their different perspectives and strengths, combining text analytics with text mining often leads to better performance than either approach alone.
2. What technologies were used in building Watson (both hardware and software)?
Watson is an extraordinary computer system (a novel combination of advanced hardware an software) designed at answering questions posed in natural human language.Watson is an artificially intelligent computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first CEO and industrialist Thomas J. Watson. The computer system was specifically developed to answer questions on the quiz show Jeopardy! In 2011, Watson competed on Jeopardy! against former winners Brad Rutter and Ken Jennings.
Watson received the first prize of $1 million.The goal was to advance computer science by exploring new ways for computer technology to affect science, business, and society.IBM undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV quiz show Jeopardy!The extent of the challenge in ...
Post 1What is text analytics How does it differ from text minianhcrowley
Post 1:
What is text analytics? How does it differ from text mining?
Text Analytics is applying of statistical and machine learning techniques to be able to predict /prescribe or infer any information from the text-mined data. Text mining is a tool that helps in getting the data cleaned up.Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data.
Differences between Text Mining and Text Analytics:
• Text Mining and Text Analytics solve the same problems, but use different techniques and are complementary ways to automatically extract meaning from text.
• Text Analytics is developed within the field of computational linguistics. It has the ability to encode human understanding into a series of linguistic rules which are generated by humans are high in precision, but they do not automatically adapt and are usually fragile when tried in new situations.
• Text mining is a newer discipline arising out of the fields of statistics, data mining, and machine learning. Its strength is the ability to inductively create models from collections of historical data. Because statistical models are learned from training data they are adaptive and can identify “unknown unknowns”, leading to the better recall. Still, they can be prone to missing something that would seem obvious to a human.
• Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data.
• Due to their different perspectives and strengths, combining text analytics with text mining often leads to better performance than either approach alone.
2. What technologies were used in building Watson (both hardware and software)?
Watson is an extraordinary computer system (a novel combination of advanced hardware an software) designed at answering questions posed in natural human language.Watson is an artificially intelligent computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first CEO and industrialist Thomas J. Watson. The computer system was specifically developed to answer questions on the quiz show Jeopardy! In 2011, Watson competed on Jeopardy! against former winners Brad Rutter and Ken Jennings.
Watson received the first prize of $1 million.The goal was to advance computer science by exploring new ways for computer technology to affect science, business, and society.IBM undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV quiz show Jeopardy!The extent of the challenge in ...
A novel approach for text extraction using effective pattern matching techniqueeSAT Journals
Abstract
There are many data mining techniques have been proposed for mining useful patterns from documents. Still, how to effectively use and update discovered patterns is open for future research , especially in the field of text mining. As most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy(words have multiple meanings) and synonymy(multiple words have same meaning). People have held hypothesis that pattern-based approaches should perform better than the term-based, but many experiments does not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern matching, to improve the effective use of discovered patterns.
Keywords: Pattern Mining, Pattern Taxonomy Model, Inner Pattern Evolving, TF-IDF, NLP etc.
Great model a model for the automatic generation of semantic relations betwee...ijcsity
The
large
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ailable
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su
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as
healthcare
(e.g.
medical
records),
justice
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l
a
ws,
declarations),
insurance
(e.g.
declarations),
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the
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summaries
do
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v
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w
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within
the
domains
that
require
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k
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to
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the
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of
the
text
(e.g.
a
rece
n
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text
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a
medical
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more
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orta
n
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a
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and
the
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p
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ecialization).
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w
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b
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disc
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and
h
y
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Se
v
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tests
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executed
su
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as
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unctional
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Usabili
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erformance
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-
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b
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pr
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viding
a
text
more
shorter than
the
original
Similar to Dynamic & Attribute Weighted KNN for Document Classification Using Bootstrap Sampling (20)
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Cosmetic shop management system project report.pdf
Dynamic & Attribute Weighted KNN for Document Classification Using Bootstrap Sampling
1. Dharmendra S Panwar Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 11(Version - 6), November 2014, pp.30-36
www.ijera.com 30 | P a g e
Dynamic & Attribute Weighted KNN for Document Classification
Using Bootstrap Sampling
Dharmendra S Panwar, Kshitij Pathak
Mahakal Institute of Technology Ujjain
Abstract—
Although publicly accessible databases containing speech documents. It requires a great deal of time and effort
required to keep them up to date is often burdensome. In an effort to help identify speaker of speech if text is
available, text-mining tools, from the machine learning discipline, it can be applied to help in this process also.
Here, we describe and evaluate document classification algorithms i.e. a combo pack of text mining and
classification. This task asked participants to design classifiers for identifying documents containing speech
related information in the main literature, and evaluated them against one another. Expected systems utilizes a
novel approach of k -nearest neighbour classification and compare its performance by taking different values of
k.
Keywords— Data Mining, Text Mining, Classification, K-nearest neighbour, KNN
I. INTRODUCTION
Text mining is a prosper new field that attempts
to extract meaningful information from natural
language text. It can be characterized as the process
of analysing unstructured text to extract or retrieve
information that is useful for particular purposes.
Compared with other type of data stored in databases,
algorithmically dealing with text is very difficult
because text is irregular and unstructured. In modern
or recent culture, text is the most and common way
for the formal exchange of information. Emphasis in
text mining is to find hidden patterns or opinions and
extraction of information related to communication
from an individual or parties. Mining the text is
fruitful even if it provides partial success.
II. TEXT MINING
Recently text mining has become an important
research area. Text can be placed in organization
files, mails, online chats, product reviews,
newspaper articles, journal and SMS. Text mining
can also be called as text data mining, intelligent text
analysis or knowledge discovery in text (KDT)
refers to extracting the useful information from the
natural language text. Text mining extracts the new
pieces of knowledge from textual data.
Fundamentally text mining is used to joint countless
pages of plain-language digitized text to calculate
useful information that has been hiding in plain sight
(Gary Belsky: 2012).
Approximately 80% of the world’s data is in not
structured format. Data are stored in most of the
government sectors, organization, industries and
institutes in electronic form. These all data are stored
in text database format. Text database is semi
structured format which contains many structured
fields and few unstructured fields. For example
students name, roll no, class, semester are the
structured fields and Address, remarks are
unstructured fields in an educational institution. Text
mining is very essential for an organization or
institution because most of the information in the
organizations is in text format.
Text Mining [1] is the discovery by computer of new,
previously unknown information, by automatically
extracting information from different written
resources.
Figure 2.1 Text Mining Basic
A main element is linking to together to form new
facts or new conclusion to be research further by
conventional means of experimentation. Text mining
is different from the web search. In web search, the
user is looking for something that is already known
and which has been written by someone. The
problem is pushing aside all the material that
currently is not relevant to your needs in order to
find the relevant information. In text mining, the aim
is to discover unknown information from the
RESEARCH ARTICLE OPEN ACCESS
2. Dharmendra S Panwar Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 11(Version - 6), November 2014, pp.30-36
www.ijera.com 31 | P a g e
datasets that no one yet knows and so could not have yet written down. Text mining is a part of data mining [2] that tries to find interesting patterns from large databases and data warehouse. Text mining can also be known as Knowledge-Discovery in Text (KDT), Text Data Mining or Intelligent Text Analysis is the process of extracting interesting and valuable information and knowledge from unstructured text. Text mining is a new and young interdisciplinary field which draws on data mining, information retrieval, machine learning, computational and statistics linguistics. As per the study, 80% information is stored as text form, text mining is considered to have a big commercial potential value. Knowledge may be discovered from many or multiple sources of information but unstructured text remains the largest readily available source of knowledge. The problem of Knowledge Discovery from Text (KDT) [3] is to extract explicit and implicit concepts and semantic relations between concepts using Natural Language Processing techniques (NLP). Its purpose to get insights into large quantities of text data. Knowledge Discovery from Text, while deeply rooted in Natural Language Processing, draws on methods from statistics, information extraction, machine learning, reasoning, knowledge management, and others for its discovery process. KDT plays an important and increasingly significant role in emerging applications for example Text Understanding. Text mining [1] is just like of data mining, except that data mining tools [2] are designed to work with structured data from databases, but text mining can apply with unstructured, semi-structured and structured data sets such as Internet files (HTML), emails and full text files etc. As a result, text mining is a better solution for companies. To date, however, most research and development efforts have centered on data mining efforts using structured data. The problem introduced by text mining is obvious: natural language was developed for humans to communicate with one another and to record and save information, and computers are a large way from comprehending natural language. Figure 2.2 An Example of Text Mining
Humans have the ability to distinguish and apply linguistic patterns to text and humans can easily overcome obstacles that computers cannot easily handle such as contextual meaning slang and spelling variations. However, although our language capabilities allow us to comprehend unstructured data and lacking of the computer’s ability to process unstructured text in large volumes or at high speeds. Figure 2.2 shows a generic process model [4] for a text mining application. Starting with a collection of documents and a text mining tool would be retrieve a specific document and pre-process it by checking character sets and format. Then it would be go through by a text analysis phase and repeating techniques of mining until information is extracted. The final resulting information would be placed in a management information system and that helps to yielding an abundant amount of knowledge that is very useful for the user of that system.
III. CLASSIFICATION
All Classification, which is the task of assigning objects to one of several predefined categories or class, is a pervasive problem that encloses many diverse applications. Examples that include classifying galaxies based upon their shapes, categorizing cells as malignant or benign based upon the results of MRI scans and detecting spam email messages based upon the message content and header. Figure 3.1 Classification as the task of mapping an input attribute set x into its class label y The input data for a classification task is a set of records. Every record also know as instance or can characterized by a tuble(x,y),where x is attribute set and y is a unique attribute as class label also know as target or category attribute. Mainly Classification is the task of learning a target function f that Assign each attribute of set x to one of the predefined class labels y. The target function is also known informally as a classification model. A classification model is useful for the following purposes:- Descriptive Modeling: It is a classification model that serves as an informing tool to distinguish between objects of different classes. Predictive Modeling: It is a classification model that also be used to predict the class label of unknown records.
Classification techniques are most suited for predicting or describing or defining data sets with nominal or binary categories. They are less effective for ordinal categories (e.g., to classify a person in terms of their income group like high, medium or low income group) because they do not consider the implicit order among the categories. Other forms of
3. Dharmendra S Panwar Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 11(Version - 6), November 2014, pp.30-36
www.ijera.com 32 | P a g e
relationships, such as the subclass–super class relationships among categories (e.g., apes and humans are primates which is a subclass of mammals) are also ignored. Following are the general Approach to Solving a Classification Problem
A classification technique (or classifier) is a systematic approach to building classification models from an input data set. Examples include decision neural networks, naıve Bayes classifiers, rule-based classifiers, tree classifiers, support vector machines. Each technique uses learning algorithm to identify a model that best fits the relationship between class label and the attributes set of the input datasets. The algorithm generated model should both fit the input data and correctly predict the class labels of records it hasn’t seen before. That’s why a main objective of the learning algorithm is to build models with good generalization capability; that is to say, models that accurately predict the class labels of previously unknown records.
Figure 3.2 shows a general approach for solving classification problems. First, a training set containing the records whose class labels are known must be given. Figure 3.2 General approach for building a classification model. Table 3.1 Confusion matrix for a 2-class problem The training set is used to generate a classification model that is applied to the test dataset, which consist unknown class labels records. The performance and evaluation of the model is based on correctly or incorrectly predict the counts of the test records. These counts are tabulated in a table known as a confusion matrix. Table 3.1 depicts the confusion matrix for a binary classification problem. Each entry fij in this table shows the number of records from class i predicted to be of class j. Here f01 is the number of records from class 0 incorrectly predicted as class 1. On the basis of the entries in the confusion matrix and the total number of correct predictions made by the model is (f11+f00) and the total number of incorrect predictions is (f10+f01). Confusion matrix provides the useful information that determine how well model perform or work and its summarized this information with single number that it more convenient to compare with different models performance. This can also be done using a performance metric like that accuracy, which is defined as follows:
4. Dharmendra S Panwar Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 11(Version - 6), November 2014, pp.30-36
www.ijera.com 33 | P a g e
Likewise, the performance of a model can be calculated in terms of its error rate, using the following equation:
Most of the classification algorithms seek models that attain the highest accuracy, or equivalently, the lowest
error rate when applied to the test set.
K-Nearest Neighbors (KNN)
One of the various classifier, 'KNN classifier' is a case based learning algorithm which is based on a distance or
similarity function for various pairs of observation such as the Euclidean distance function. It is tried for many
applications because of its effectiveness, non-parametric & easy to implementation properties. However, under
this method, the classification time is very long & it is difficult to find optimal value of K. Generally, the best
alternative of k to be chosen depends on the data. Also, the effect of noise on the classification is reduced by the
larger values of k but make boundaries between classes less distinct. By using various heuristic techniques, a
good 'k' can be selected. In order to overcome the above said drawback, modify traditional KNN with different
K values for different classes rather than fixed value for all classes.
How KNN Algorithm Works
KNN algorithm is used to classify instances based on nearest training examples in the frame space. KNN
algorithm is known as lazy learning algorithm in which function is approximated locally & computations are
delayed until classification. A majority of instances is used for classification process. Object is classified into
the particular class which has maximum number of nearest instances.
Fig 3.3 Example of k- nearest neighbor [11]
In above figure the test instance (green circle) should be classified either into blue square class or into red
triangle class. If k = 3 (solid line circle) test object (green circle) is classified into red triangle class because
there are 2 triangle instances and only 1 square instance in the inner circle.
If k = 5 (dashed line circle) test object (green circle) is classified into blue square class because there are 3 blue
square instances and only 2 red triangle instances in the inner circle.
SCOPE OF KNN
Improvement of KNN can be done on following parameters. [12]
A. Distance or similarity Function: The distance or similarity function is used for measuring the difference or
similarity between two instances is the standard Euclidean distance.
B. Selection of K Value: It represents neighbourhood size, which is artificially assigned as an input parameter.
C. Calculating Class Probability: The class probability assumption, based on a simple voting.
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IV. BOOTSTRAP SAMPLING
Bootstrap methods are more than 30 years old
[8], but they are computer-intensive and have only
recently become widely available in statistical
programs. Powerful and widely available computing
capability is transforming statistics. Originally,
computers just speeded up data handling and
statistical computations. Now they enable exploratory
analysis of gigantic data sets (data mining), creating
neural network algorithms, analysing complex
genomic data records, and supplementing traditional
statistical methods using large numbers of data
simulations.
As per statistics, bootstrapping is a method for
assigning measures of accuracy to sample estimates.
This technique also allows estimation of the sampling
distribution of almost any statistic using only very
simple methods.[9,10] Generally, it falls in the
broader class of resampling methods.
Bootstrapping is the process of estimating properties
of an estimator (like variance) by measuring those
properties when sampling from an approximating
distribution.
To understand bootstrap, suppose it were possible to
draw repeated samples (of the same size) from the
population of interest, a large and more number of
times. Then one would obtain a fairly good idea
about the sampling distribution of a particular
statistic from the collection of its values arising from
these repeated samples. But, that does not mean that
it would be too expensive and defeat the purpose of a
sample study. The aim of a sample study is to collect
information cheaply in a timely fashion. The main
idea behind bootstrap sampling is to use the data of a
sample study at hand as a ―surrogate population‖, for
the aim of approximating the sampling distribution of
a statistic; i.e. to resample (with replacement) from
the sample data at hand and create a large number of
―phantom samples‖ known as bootstrap samples. The
summary of the sample is then computed on each of
the bootstrap samples (usually a few or more
thousand). A histogram of the set of these computed
values is referred to as the bootstrap distribution of
the statistic.
V. TEXT CLASSIFICATION
The Text classification is a supervised learning
algorithm whose approach is to classify a text
documents into pre-defined categories [5].
Classification has basic four types as per applications
– Binary Hard, Multiclass single label, Multi-class
Multi label hard and multi-class multi label soft
classification [6].
Figure -5.1 General model of text classification
Multi-class means multi categories are allowed like sport, jokes, and business in news data. Multi –label means
document belong to more than one category like any documents belong to business and sport categories. Hard
classification means document has Boolean membership into classes and soft means fuzzy membership into all
classes. In this paper, our approach is regarding multi-class single label classification.
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For classification, general model is represented in figure -5.1. There are two phases - training and testing phase. In training phase, training documents that are pre-labeled are used to build a model using its feature vectors which are generated by pre-processing. Actual class assignment process performed in test phase. Whenever a new test document come for classification, then feature vector of test document is applied in model and class label assigned. In our literature research we found that most methods and developments can be divided into following distinct steps. The steps are: 1. Text gathering, 2. Text pre-processing, i. Stopword ii. Stemming iii. Pruning 3. Document Indexing, 4. Pattern Mining and Result Evaluation i. Categorization a. Sentiment Classification b. Authorship identification c. Web classification ii. Information Retrieval
VI. PROPOSED WORK
The simplicity and robustness of k-NN has been evident, and has been discussed already. This work target on one of the most vital factors of k-NN, namely, the selection of the value of k. Various experiments using different adaptations of the traditional k-NN to both improve its accuracy, and address its shortcomings have been researched in [7]. With the increasing variety of document, and the complex nature displayed by these document pages, a dynamic value of k is of ever increasing importance. In this work value of k is set dynamically and classify the document using dynamic weighted K-nearest neighbor classifier using bootstrap sampling. In the proposed work, first set the value of k which is dynamically and classify the document using dynamic weighted K-nearest neighbour classifier using bootstrap sampling. Algorithm 6.1 Dynamic & Attribute Weighted KNN for Document Classification Using Bootstrap Sampling (DAWk-nn) Phase-I
1. Clean Corpus by // Corpus: Collection of documents.
i. Removing Punctuation
ii. Removing White space
iii. Converting text to either upper or lower case
iv. Removing Stopwords
2. Make Term document matrix.
3. Remove sparse terms.
4. Let r=maximum expected value of K
For i=1 to r
i. Take a bootstrap sample
ii. Evaluate the performance by simple distance based KNN
iii. Stored in performance matrix (performance[i])
5. Select the best value from performance array & store it in K. Phase-2 1. Again apply bootstrap sampling on original dataset, divide dataset into training & testing module. 2. Clean Corpus by
i. Punctuation
ii. White space
iii. Either select upper or lower case
iv. Stopwords
3. Make document matrix 4. Remove sparse terms 5. Assign the Weight to training dataset by using following equation:- Wr= 1/ [d (xi, xq)2 + 0.001] 6. Calculate Distance using K-NN algorithm with the help of K Picked in Step 5.
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7. Return the class label.
In this algorithm, in Phase-1, first step we have to clean the raw data or unstructured text that is called clean corpus by removing punctuation, removing white space, converting text to either upper or lower case, removing stopwords. In the next step make the document matrix and remove the sparse terms. In the fourth step calculate the value of K for proposed work using bootstrap sampling and performance calculation and after the select the best K in performance matrix. When get the value of K from Phase-1, use it in Phase-2 for calculating beast nearest neighbor. Now Phas-2, again we apply the bootstrap sampling but in this time work on original dataset and clean that original data sets using clean corpus, make document matrix, remove sparse terms. In fifth step Find the best K nearest neighbor to meet training dataset by using following equation:- Wr= 1/ [d (xi, xq)2 + 0.001] After calculate the weight return the class label.
VII. CONCLUSION
In this proposed work, we investigated the factors that reduced the time complexity of document classification. The proposed approach overcomes the limitation of traditional K-nearest neighbor by incorporating neighbor weighted and dynamically picking K to provide best result, this proposed work also use the bootstrap sampling technique which is the method for assigning measure of accuracy to sample estimate. We have measured accuracy of the proposed approach with the standard K-nearest neighbor on 20,000 newsgroup documents dataset downloaded from [13] and accuracy of the classifier increases by 11.32 % ( on average ) in comparison to the earlier approach. REFERENCES
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[7] Jiang L., Cai Z., Wang D., and Jiang S (2007), ―Survey of Improving K-Nearest- Neighbor for Classification‖. In Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD ’07), vol. 1, pp. 679-683.
[8] Efron B. Bootstrap methods: another look at the jackknife. Ann Statist 1979;7:1–26.
[9] Varian, H.(2005). "Bootstrap Tutorial". Mathematica Journal, 9, 768-775.
[10] Weisstein, Eric W. "Bootstrap Methods." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/BootstrapMethods.html
[11] K-nearest neighbor algorithm http://en.wikipedia.org/wiki/K- earest_neighbor_algorithm.
[12] Jia Wu, Zhihua Cai, Zhechao, Gao (2010), ―Dynamic K-Nearest-Neighbor with Distance and Attribute Weighted for Classification,‖ International Conference on Electronics and Information Engineering (ICEIE), 978-1- 4244-7681 IEEE 2010.
[13] http://web.ist.utl.pt/acardoso/datasets/ ( Visited before 2 Month before)