Pub Date : 2016-08-25DOI: 10.1109/CSIT.2016.7549480
Noor Siti Husnah Ab Rahim Periamalai, A. Mustapha, Ahmad Alqurneh
This paper reports the development of an ontology for Juz' Amma in the Quran manuscript that is designed based on the contextual information support sourced from expert knowledge. The ontology development adopts an existing methology called the Methontology that covers steps from identifying motivation scenarios, formulating the competency questions, development, and evaluation. The ontology was evaluated based on the competency questions determined at the beginning of the development life cycle and the results were promising. The developed ontology is hoped to serve as the domain knowledge for other applications such as the question-answering, dialogue or expert systems.
{"title":"An ontology for Juz' Amma based on expert knowledge","authors":"Noor Siti Husnah Ab Rahim Periamalai, A. Mustapha, Ahmad Alqurneh","doi":"10.1109/CSIT.2016.7549480","DOIUrl":"https://doi.org/10.1109/CSIT.2016.7549480","url":null,"abstract":"This paper reports the development of an ontology for Juz' Amma in the Quran manuscript that is designed based on the contextual information support sourced from expert knowledge. The ontology development adopts an existing methology called the Methontology that covers steps from identifying motivation scenarios, formulating the competency questions, development, and evaluation. The ontology was evaluated based on the competency questions determined at the beginning of the development life cycle and the results were promising. The developed ontology is hoped to serve as the domain knowledge for other applications such as the question-answering, dialogue or expert systems.","PeriodicalId":210905,"journal":{"name":"2016 7th International Conference on Computer Science and Information Technology (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127861683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-13DOI: 10.1109/CSIT.2016.7549460
Mansoor Alghamdi, Ibrahim Alkhazi, W. Teahan
Performance evaluation of Optical Character Recognition (OCR) systems is an essential task for OCR systems development. However, studies in Arabic OCR suffer from the lack of proper performance evaluation metrics and the availability of evaluation tools. Although the literature provides typical performance metrics, such as character accuracy and word accuracy for OCR performance evaluation, these metrics are not sufficient for evaluating Arabic OCR. This paper presents an open source automated software tool with various metrics for the evaluation of Arabic OCR performance. The developed tool is available for OCR researchers, thus it can be applied for ranking different OCR algorithms.
{"title":"Arabic OCR evaluation tool","authors":"Mansoor Alghamdi, Ibrahim Alkhazi, W. Teahan","doi":"10.1109/CSIT.2016.7549460","DOIUrl":"https://doi.org/10.1109/CSIT.2016.7549460","url":null,"abstract":"Performance evaluation of Optical Character Recognition (OCR) systems is an essential task for OCR systems development. However, studies in Arabic OCR suffer from the lack of proper performance evaluation metrics and the availability of evaluation tools. Although the literature provides typical performance metrics, such as character accuracy and word accuracy for OCR performance evaluation, these metrics are not sufficient for evaluating Arabic OCR. This paper presents an open source automated software tool with various metrics for the evaluation of Arabic OCR performance. The developed tool is available for OCR researchers, thus it can be applied for ranking different OCR algorithms.","PeriodicalId":210905,"journal":{"name":"2016 7th International Conference on Computer Science and Information Technology (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115649199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-13DOI: 10.1109/CSIT.2016.7549458
Abdullateef Rabab'ah, M. Al-Ayyoub, Y. Jararweh, M. Al-Kabi
Social networking websites are used today as platforms enabling their users to write down almost anything about everything. Social media users express their opinions and feelings about lots of events occurring in their daily lives. Lots of studies are conducted to study the sentiments presented by social media users regarding different topics. Sentiment Analysis (SA) is a new field that is concerned with measuring the sentiment presented in a given text. Due to their wide set of applications, several SA tools are available. Most of them are designed for English text. As for other languages such as Arabic, the case is different since only few tools are available. In fact, many of these tools were originally designed for English and were later adapted to deal with Arabic. SentiStrength is an example of tools that are successful for English and were later adapted to Arabic. However, the adaptation has been done in a crude manner and no deep studies are available to measure the effectiveness of such tools for Arabic text. In this paper, we perform a comprehensive evaluation of SentiStrength using 11 Arabic datasets consisting of tens of thousands of reviews/comments from different domains and in different dialects. We perform the evaluation in terms of positive and negative sentiments. The evaluation results show that overall SentiStrength achieves 62% accuracy, 83.7% precision, 64% recall (positive correct), 68% F1 measure and 55% negative correct.
{"title":"Evaluating SentiStrength for Arabic Sentiment Analysis","authors":"Abdullateef Rabab'ah, M. Al-Ayyoub, Y. Jararweh, M. Al-Kabi","doi":"10.1109/CSIT.2016.7549458","DOIUrl":"https://doi.org/10.1109/CSIT.2016.7549458","url":null,"abstract":"Social networking websites are used today as platforms enabling their users to write down almost anything about everything. Social media users express their opinions and feelings about lots of events occurring in their daily lives. Lots of studies are conducted to study the sentiments presented by social media users regarding different topics. Sentiment Analysis (SA) is a new field that is concerned with measuring the sentiment presented in a given text. Due to their wide set of applications, several SA tools are available. Most of them are designed for English text. As for other languages such as Arabic, the case is different since only few tools are available. In fact, many of these tools were originally designed for English and were later adapted to deal with Arabic. SentiStrength is an example of tools that are successful for English and were later adapted to Arabic. However, the adaptation has been done in a crude manner and no deep studies are available to measure the effectiveness of such tools for Arabic text. In this paper, we perform a comprehensive evaluation of SentiStrength using 11 Arabic datasets consisting of tens of thousands of reviews/comments from different domains and in different dialects. We perform the evaluation in terms of positive and negative sentiments. The evaluation results show that overall SentiStrength achieves 62% accuracy, 83.7% precision, 64% recall (positive correct), 68% F1 measure and 55% negative correct.","PeriodicalId":210905,"journal":{"name":"2016 7th International Conference on Computer Science and Information Technology (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120856931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-13DOI: 10.1109/CSIT.2016.7549449
Daniel Müller, Funk Te, Flavien Meyer, Irena Pletikosa Cvijikj
In Switzerland small and medium-sized enterprises represent more than 99% of all businesses. Therefore, prediction of their micro- and macroeconomic business development is of importance. In this paper, we propose a novel approach for predicting business volume using company characteristics and characteristics of the county the company operates in. We investigate which data sources can be combined to achieve this goal for small and midsized enterprises in Switzerland, building a model, irrespective of industry. We build our model based on the dataset obtained from an insurance company and combined the dataset with census data. We present two quantitative models, which allow to predict business volume in Swiss franks (CHF) and classify customers by size. Our results show that operational data from financial institutions (FI) customer relationship management (CRM) systems linked with census data are valuable to predict customer business volume.
{"title":"Towards data driven decision support for financial institutions: Predicting small companies business volume in Switzerland","authors":"Daniel Müller, Funk Te, Flavien Meyer, Irena Pletikosa Cvijikj","doi":"10.1109/CSIT.2016.7549449","DOIUrl":"https://doi.org/10.1109/CSIT.2016.7549449","url":null,"abstract":"In Switzerland small and medium-sized enterprises represent more than 99% of all businesses. Therefore, prediction of their micro- and macroeconomic business development is of importance. In this paper, we propose a novel approach for predicting business volume using company characteristics and characteristics of the county the company operates in. We investigate which data sources can be combined to achieve this goal for small and midsized enterprises in Switzerland, building a model, irrespective of industry. We build our model based on the dataset obtained from an insurance company and combined the dataset with census data. We present two quantitative models, which allow to predict business volume in Swiss franks (CHF) and classify customers by size. Our results show that operational data from financial institutions (FI) customer relationship management (CRM) systems linked with census data are valuable to predict customer business volume.","PeriodicalId":210905,"journal":{"name":"2016 7th International Conference on Computer Science and Information Technology (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120912196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-13DOI: 10.1109/CSIT.2016.7549474
Mawal A. Mohammed, Mahmoud O. Elish, A. Qusef
Design patterns are common solutions to specific design problems. There are many claimed benefits of the application of design patterns on design quality. This paper empirically evaluates and compares the modularity of design patterns in object-oriented software. Coupling and cohesion of classes that participate in design patterns were compared with those that do not participate. We used CBO and LCOM metrics as proxy measures for coupling and cohesion respectively. Data were collected from five open source systems, and analyses were conducted at both the design and pattern levels. At the design level, we compared the modularity of participant versus non-participant classes in design patterns, whereas at the pattern level, we compared the modularity of the classes in each pattern. The results indicate that the classes that participate in design patterns are more coupled and less cohesive than the non-participant classes at both levels.
{"title":"Empirical insight into the context of design patterns: Modularity analysis","authors":"Mawal A. Mohammed, Mahmoud O. Elish, A. Qusef","doi":"10.1109/CSIT.2016.7549474","DOIUrl":"https://doi.org/10.1109/CSIT.2016.7549474","url":null,"abstract":"Design patterns are common solutions to specific design problems. There are many claimed benefits of the application of design patterns on design quality. This paper empirically evaluates and compares the modularity of design patterns in object-oriented software. Coupling and cohesion of classes that participate in design patterns were compared with those that do not participate. We used CBO and LCOM metrics as proxy measures for coupling and cohesion respectively. Data were collected from five open source systems, and analyses were conducted at both the design and pattern levels. At the design level, we compared the modularity of participant versus non-participant classes in design patterns, whereas at the pattern level, we compared the modularity of the classes in each pattern. The results indicate that the classes that participate in design patterns are more coupled and less cohesive than the non-participant classes at both levels.","PeriodicalId":210905,"journal":{"name":"2016 7th International Conference on Computer Science and Information Technology (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124530518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-13DOI: 10.1109/CSIT.2016.7549448
A. Qusef, Khaled Ismail
Many experts agree that the greatest threat to the success of any project, especially IT projects, is a failure to communicate. Project managers say they spend as much as 90 percent of their time communicating. Just as it is difficult to understand people and their motivations, it is also difficult to communicate with people effectively. Communications software and collaboration tools like e-mail, blogs, Web sites, Google docs, and tweets can aid in stakeholder communications and promote stakeholder engagement in projects. A very popular software category todaySocial Mediacan also help engage stake-holders. This paper highlights keys to good communications, provides suggestions for improving communications using the Social Media (SM), and describes how SM can assist in project communications management.
{"title":"Social Media in project communications management","authors":"A. Qusef, Khaled Ismail","doi":"10.1109/CSIT.2016.7549448","DOIUrl":"https://doi.org/10.1109/CSIT.2016.7549448","url":null,"abstract":"Many experts agree that the greatest threat to the success of any project, especially IT projects, is a failure to communicate. Project managers say they spend as much as 90 percent of their time communicating. Just as it is difficult to understand people and their motivations, it is also difficult to communicate with people effectively. Communications software and collaboration tools like e-mail, blogs, Web sites, Google docs, and tweets can aid in stakeholder communications and promote stakeholder engagement in projects. A very popular software category todaySocial Mediacan also help engage stake-holders. This paper highlights keys to good communications, provides suggestions for improving communications using the Social Media (SM), and describes how SM can assist in project communications management.","PeriodicalId":210905,"journal":{"name":"2016 7th International Conference on Computer Science and Information Technology (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131440799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-13DOI: 10.1109/CSIT.2016.7549453
L. Abualigah, A. Khader, M. Al-Betar
The increasing amount of text documents in digital forms affect the text analysis techniques. Text clustering (TC) is one of the important techniques used for showing a massive amount of text documents by clusters. Hence, the main problem that affects the text clustering technique is the presence sparse and uninformative features on the text documents. The feature selection (FS) is an essential unsupervised learning technique. This technique is used to select informative features to improve the performance of text clustering algorithm. Recently, the meta-heuristic algorithms are successfully applied to solve several hard optimization problems. In this paper, we proposed the genetic algorithm (GA) to solve the unsupervised feature selection problem, namely, (FSGATC). This method is used to create a new subset of informative features in order to obtain more accurate clusters. Experiments were conducted using four benchmark text datasets with variant characteristics. The results showed that the proposed FSGATC is improved the performance of the text clustering algorithm and got better results compared with k-mean clustering standalone. Finally, the proposed method “FSGATC” evaluated by F-measure and Accuracy, which are common measures used in the domain of text clustering.
{"title":"Unsupervised feature selection technique based on genetic algorithm for improving the Text Clustering","authors":"L. Abualigah, A. Khader, M. Al-Betar","doi":"10.1109/CSIT.2016.7549453","DOIUrl":"https://doi.org/10.1109/CSIT.2016.7549453","url":null,"abstract":"The increasing amount of text documents in digital forms affect the text analysis techniques. Text clustering (TC) is one of the important techniques used for showing a massive amount of text documents by clusters. Hence, the main problem that affects the text clustering technique is the presence sparse and uninformative features on the text documents. The feature selection (FS) is an essential unsupervised learning technique. This technique is used to select informative features to improve the performance of text clustering algorithm. Recently, the meta-heuristic algorithms are successfully applied to solve several hard optimization problems. In this paper, we proposed the genetic algorithm (GA) to solve the unsupervised feature selection problem, namely, (FSGATC). This method is used to create a new subset of informative features in order to obtain more accurate clusters. Experiments were conducted using four benchmark text datasets with variant characteristics. The results showed that the proposed FSGATC is improved the performance of the text clustering algorithm and got better results compared with k-mean clustering standalone. Finally, the proposed method “FSGATC” evaluated by F-measure and Accuracy, which are common measures used in the domain of text clustering.","PeriodicalId":210905,"journal":{"name":"2016 7th International Conference on Computer Science and Information Technology (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124074748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-13DOI: 10.1109/CSIT.2016.7549459
Wegdan A. Hussien, Yahya M. Tashtoush, M. Al-Ayyoub, M. Al-Kabi
Nowadays, the automatic detection of emotions is employed by many applications across different fields like security informatics, e-learning, humor detection, targeted advertising, etc. Many of these applications focus on social media. In this study, we address the problem of emotion detection in Arabic tweets. We focus on the supervised approach for this problem where a classifier is trained on an already labeled dataset. Typically, such a training set is manually annotated, which is expensive and time consuming. We propose to use an automatic approach to annotate the training data based on using emojis, which are a new generation of emoticons. We show that such an approach produces classifiers that are more accurate than the ones trained on a manually annotated dataset. To achieve our goal, a dataset of emotional Arabic tweets is constructed, where the emotion classes under consideration are: anger, disgust, joy and sadness. Moreover, we consider two classifiers: Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB). The results of the tests show that the automatic labeling approaches using SVM and MNB outperform manual labeling approaches.
{"title":"Are emoticons good enough to train emotion classifiers of Arabic tweets?","authors":"Wegdan A. Hussien, Yahya M. Tashtoush, M. Al-Ayyoub, M. Al-Kabi","doi":"10.1109/CSIT.2016.7549459","DOIUrl":"https://doi.org/10.1109/CSIT.2016.7549459","url":null,"abstract":"Nowadays, the automatic detection of emotions is employed by many applications across different fields like security informatics, e-learning, humor detection, targeted advertising, etc. Many of these applications focus on social media. In this study, we address the problem of emotion detection in Arabic tweets. We focus on the supervised approach for this problem where a classifier is trained on an already labeled dataset. Typically, such a training set is manually annotated, which is expensive and time consuming. We propose to use an automatic approach to annotate the training data based on using emojis, which are a new generation of emoticons. We show that such an approach produces classifiers that are more accurate than the ones trained on a manually annotated dataset. To achieve our goal, a dataset of emotional Arabic tweets is constructed, where the emotion classes under consideration are: anger, disgust, joy and sadness. Moreover, we consider two classifiers: Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB). The results of the tests show that the automatic labeling approaches using SVM and MNB outperform manual labeling approaches.","PeriodicalId":210905,"journal":{"name":"2016 7th International Conference on Computer Science and Information Technology (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122513118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-13DOI: 10.1109/CSIT.2016.7549456
L. Abualigah, A. Khader, M. Al-Betar
The increasing amount of text information on the Internet web pages affects the clustering analysis. The text clustering is a favorable analysis technique used for partitioning a massive amount of information into clusters. Hence, the major problem that affects the text clustering technique is the presence uninformative and sparse features in text documents. The feature selection (FS) is an important unsupervised technique used to eliminate uninformative features to encourage the text clustering technique. Recently, the meta-heuristic algorithms are successfully applied to solve several optimization problems. In this paper, we proposed the harmony search (HS) algorithm to solve the feature selection problem (FSHSTC). The proposed method is used to enhance the text clustering (TC) technique by obtaining a new subset of informative or useful features. Experiments were applied using four benchmark text datasets. The results show that the proposed FSHSTC is improved the performance of the k-mean clustering algorithm measured by F-measure and Accuracy.
{"title":"Unsupervised feature selection technique based on harmony search algorithm for improving the text clustering","authors":"L. Abualigah, A. Khader, M. Al-Betar","doi":"10.1109/CSIT.2016.7549456","DOIUrl":"https://doi.org/10.1109/CSIT.2016.7549456","url":null,"abstract":"The increasing amount of text information on the Internet web pages affects the clustering analysis. The text clustering is a favorable analysis technique used for partitioning a massive amount of information into clusters. Hence, the major problem that affects the text clustering technique is the presence uninformative and sparse features in text documents. The feature selection (FS) is an important unsupervised technique used to eliminate uninformative features to encourage the text clustering technique. Recently, the meta-heuristic algorithms are successfully applied to solve several optimization problems. In this paper, we proposed the harmony search (HS) algorithm to solve the feature selection problem (FSHSTC). The proposed method is used to enhance the text clustering (TC) technique by obtaining a new subset of informative or useful features. Experiments were applied using four benchmark text datasets. The results show that the proposed FSHSTC is improved the performance of the k-mean clustering algorithm measured by F-measure and Accuracy.","PeriodicalId":210905,"journal":{"name":"2016 7th International Conference on Computer Science and Information Technology (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131909517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-13DOI: 10.1109/CSIT.2016.7549464
L. Abualigah, A. Khader, M. Al-Betar
Text documents clustering is a popular unsupervised text mining tool. It is used for partitioning a collection of text documents into similar clusters based on the distance or similarity measure as decided by an objective function. Text clustering algorithm often makes prior assumptions to satisfy objective function, which is optimized either through traditional techniques or meta-heuristic techniques. In text clustering techniques, the right decision for any document distribution is done using an objective function. Normally, clustering algorithms perform poorly when the configuration of the well-formulated objective function is not sound and complete. Therefore, we proposed multi-objectives-based method namely, combine distance and similarity measure for improving the text clustering technique. Multi-objectives text clustering method is combined with two evaluating criteria which emerge as a robust alternative in several situations. In particular, the multi-objective function in the text clustering domain is not a popular, and it is a core issue that affects the performance of the text clustering technique. The performance of multi-objectives function is investigated using the k-mean text clustering technique. The experiments were conducted using seven standard text datasets. The results showed that the proposed multi-objectives based method outperforms the other measures in term of the performance of the text clustering, evaluated by using two common clustering measures, namely, Accuracy and F-measure.
{"title":"Multi-objectives-based text clustering technique using K-mean algorithm","authors":"L. Abualigah, A. Khader, M. Al-Betar","doi":"10.1109/CSIT.2016.7549464","DOIUrl":"https://doi.org/10.1109/CSIT.2016.7549464","url":null,"abstract":"Text documents clustering is a popular unsupervised text mining tool. It is used for partitioning a collection of text documents into similar clusters based on the distance or similarity measure as decided by an objective function. Text clustering algorithm often makes prior assumptions to satisfy objective function, which is optimized either through traditional techniques or meta-heuristic techniques. In text clustering techniques, the right decision for any document distribution is done using an objective function. Normally, clustering algorithms perform poorly when the configuration of the well-formulated objective function is not sound and complete. Therefore, we proposed multi-objectives-based method namely, combine distance and similarity measure for improving the text clustering technique. Multi-objectives text clustering method is combined with two evaluating criteria which emerge as a robust alternative in several situations. In particular, the multi-objective function in the text clustering domain is not a popular, and it is a core issue that affects the performance of the text clustering technique. The performance of multi-objectives function is investigated using the k-mean text clustering technique. The experiments were conducted using seven standard text datasets. The results showed that the proposed multi-objectives based method outperforms the other measures in term of the performance of the text clustering, evaluated by using two common clustering measures, namely, Accuracy and F-measure.","PeriodicalId":210905,"journal":{"name":"2016 7th International Conference on Computer Science and Information Technology (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126343476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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