A Comparative Study on Various Text Mining Algorithms in Data Mining

With the overwhelming increase in the amount of texts on the web, it is almost impossible for people to keep abreast of up-to-date information. Text mining is a process by which interesting information is derived from text through the discovery of patterns and trends. Text mining algorithms are used to guarantee the quality of extracted knowledge. However, the extracted patterns using text or data mining algorithms or methods leads to noisy patterns and inconsistency. Thus, different challenges arise, such as the question of how to understand these patterns, whether the model that has been used is suitable, and if all the patterns that have been extracted are relevant. Furthermore, the research raises the question of how to give a correct weight to the extracted knowledge. To address these issues, this paper presents a text post-processing method, which uses a pattern co-occurrence matrix to find the relation between extracted patterns in order to reduce noisy patterns. The main objective of this paper is not only reducing the number of closed sequential patterns, but also improving the performance of pattern mining as well. The experimental results on Reuters Corpus Volume 1 data collection and TREC filtering topics show that the proposed method is promising.

It is seen that distance education environments are more frequently used due to the progress of internet today. The data acquired from these environments are commonly irregular journal records and text-based communication messages. Digital mining applications are used in order to convert these irregular data into regular, and cleaning out the information stacks. The purpose of this research is to reveal the usage of the types of digital mining application, which are being applied within the distance education environments. For this purpose, a situation-based study was carried out which was related to the subjects of data mining, web mining and text mining, being used in the distance education environments. As a result of the research, a relationship was determined between the types of digital mining. Key words: Text mining, data mining, web mining.

News is new information or information about something that is currently happening, presented in the form of print media, broadcasts, social media or word of mouth to third parties or multiple people. The spread of this news is now very much happening both from social media or messages. However, the problem is that no one can guarantee the truth of the news received. Text mining is the application of data mining concepts and techniques to look for patterns in text, the process of analyzing text to find useful information for a particular purpose. Another definition related to text mining is that text mining is data mining in the form of text where the data source is usually obtained from documents and the goal is to find words that can represent the contents of the document so that an analysis of the connectivity between documents can be carried out. To make it easier to identify news hoax requires a text mining algorithm, using a text mining algorithm can be useful to get real news. Based on the calculations of the TF-IDF algorithm, it can be concluded that the similarity of the meaning of the news whose truth is not yet known is closest to the news ofmerahputih.com with a confidence level of 23.11%.

of works held in research and academic libraries and other relevant institutions, for research and private study; * clarify the current EU exception permitting the use of works that were made to be permanently located in the public space (the 'panorama exception'), to take into account new dissemination channels.

There is global attention on new data analytic methods. Data scraping (a typical first step for advanced data analytics), text and data mining (TDM, the extraction of knowledge from data) and machine learning (ML, often also simply referred to as Artificial Intelligence or AI) are seen as critical technologies. The legal issues involved in the regulation of data range from privacy and data protection (such as the GDPR) to proprietary approaches (such as copyright, database rights, or proposed new rights in data themselves). This paper focusses on one specific intervention, the introduction of two exceptions for text and data mining in the Directive on Copyright in the Digital Single Market (CDSM). Art. 3 is a mandatory exception for text and data mining (TDM) for the purposes of scientific research; Art. 4 permits text and data mining by anyone but with rightsholders able to “contract-out” (Art. 4), for example preventing TDM use of publicly available online content by “machine-readable means”. We trace the context of using the lever of copyright law to enable emerging technologies and support innovation. Within the EU copyright intervention, elements that may underpin a transparent legal framework for AI are identified, such as the possibility of retention of (permanent) copies for further verification. On the other hand, we identify several pitfalls, including an excessively broad definition of TDM which makes the entire field of data-driven AI development dependent on an exception. We analyse the implications of limiting the scope of the exceptions to the right of reproduction (which leaves the communication of research results in a grey zone). We also argue that the limitation of Art. 3 to certain beneficiaries remains problematic; and that the requirement of lawful access is difficult to operationalize. In conclusion, we argue that there should be no need for a TDM exception for the act of extracting informational value from protected works. The EU’s CDSM provisions paradoxically may favour the development of biased AI systems due to price and accessibility conditions for accessing training data that offer the wrong incentives. We also identify some old and new areas of the EU acquis which will play a crucial role in the future relationship of EU copyright law with technological innovation.

Resumen: La Directiva 2019/790 del Parlamento Europeo y del Consejo de 17 de abril de 2019 sobre los derechos de autor y derechos afines en el mercado único digital ha implementado ciertas excepciones sobre la minería de textos y datos. Pese a que, a priori, podría parecer que se ofrece un impulso importante a este tipo de actividades sus efectos en la práctica quedan mitigados por el encorsetamiento de su formulación que, incluso, es susceptible de generar distorsiones competitivas. Este artículo pretende dar una visión sucinta y crítica sobre estas nuevas excepciones y plantear ciertas vías de mejora futura.Palabras clave: Big Data, minería de textos y datos, Internet de las cosas, Inteligencia Artificial, Mercado Único Digital, Directiva 2019/790, excepciones al derecho de autor, “uso justo”, regla de los tres pasos, doctrina de las facilidades esenciales, competencia, innovación.Abstract: Directive 2019/790 of the European Parliament and of the Council of 17 April 2019 on copyright and related rights in the digital single market has implemented certain exceptions on text and data mining. Although these exceptions might seem to provide a significant boost to this type of activities, their effects in practice are mitigated by the tightening of its wording, which is even likely to generate competitive distortions. This article aims to give a succinct and critical review of these new exceptions and to suggest some ways of improvement for the near future.Keywords: Big Data, Text and Data Mining (TDM), IoT, AI, Single Digital Market, Directive 2019/790, copyright limitations, “fair use”, “three-steps doctrine”, esencial facilities doctrine, competition, innovation.

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, where as the SPJ method does not. In a relational database, especially with normalized tables, a significant effort is required to prepare a summary data set that can be used as input for a data mining or statistical algorithm. Most algorithms require as input a data set with a horizontal layout, with several Records and one variable or dimension per column. That is the case with models like clustering, classification, regression and PCA; consult. Each research discipline uses different terminology to describe the data set. In data mining the common terms are point-dimension. Statistics literature generally uses observation-variable. Machine learning research uses instance-feature. This article introduces a new class of aggregate functions that can be used to build data sets in a horizontal layout (denormalized with aggregations), automating SQL query writing and extending SQL capabilities. We show evaluating horizontal aggregations is a challenging and interesting problem and we introduced alternative methods and optimizations for their efficient evaluation. II. MOTIVATION As mentioned above, building a suitable data set for data mining purposes is a time- consuming task. This task generally requires writing long SQL statements or customizing SQL Code if it is automatically generated by some tool. There are two main ingredients in such SQL code: joins and aggregations; we focus on the second one. The most widely- known aggregation is the sum of a column over groups of rows. Some other aggregations return the average, maximum, minimum or row count over groups of rows. There exist many aggregations functions and operators in SQL. Unfortunately, all these aggregations have limitations to build data sets for data mining purposes. The main reason is that, in general, data sets that are stored in a relational database (or a data warehouse) come

Public science is critical to the economy and to society. However, much of the beneficial impact of scientific research only occurs when scientific knowledge is disseminated broadly and is used by others. This thesis examines the emerging policy, law, and practice of facilitating open access to scientific research data. One particular focus is to examine the open data policies recently introduced by research funders and publishers, and the potential in these for driving the practice of open scientific data into the future. This thesis identifies five major stumbling blocks to sustainable open scientific data. Firstly, the prevailing ‘mindset’ that facilitating open access to data is analogous to facilitating open access to publications and, therefore, research data can easily be shared, with research funders and librarians effectively leading the process. Secondly, the unclear meaning of the term ‘data’, which causes confusion among stakeholders. Thirdly, ‘misunderstood incentives’ for data sharing and the additional inputs required from researchers. Fourthly, ‘data privacy’—an issue that only applies to selected research datasets, and yet appears to dominate the discussion about open research data. Finally, there is ‘copyright law’, which poses challenges at different stages of data release and reuse. In this thesis, I argue that the above problems can be addressed using a staged model for open scientific data. I draw specifically on the practice with open scientific data at CERN (the European Organization for Nuclear Research) and the practice of sharing clinical trial data to argue that open data can be shared at various stages of processing and diversification. This model is supplemented by recommendations proposing changes to existing open data mandates and the introduction of a text and data mining exemption into Australian copyright law.

DEFINING TEXT MINING Text mining can be broadly defined as a knowledge-intensive process in which a user interacts with a document collection over time by using a suite of analysis tools. In a manner analogous to data mining, text mining seeks to extract useful information from data sources through the identification and exploration of interesting patterns. In the case of text mining, however, the data sources are document collections, and interesting patterns are found not among formalized database records but in the unstructured textual data in the documents in these collections. Certainly, text mining derives much of its inspiration and direction from seminal research on data mining. Therefore, it is not surprising to find that text mining and data mining systems evince many high-level architectural similarities. For instance, both types of systems rely on preprocessing routines, pattern-discovery algorithms, and presentation-layer elements such as visualization tools to enhance the browsing of answer sets. Further, text mining adopts many of the specific types of patterns in its core knowledge discovery operations that were first introduced and vetted in data mining research. Because data mining assumes that data have already been stored in a structured format, much of its preprocessing focus falls on two critical tasks: Scrubbing and normalizing data and creating extensive numbers of table joins. In contrast, for text mining systems, preprocessing operations center on the identification and extraction of representative features for natural language documents.

Abstract- Utilization data mining in banking marketing strategy is very effective. Prospective customer seg-mentation is one of the processes carried out in the banking marketing strategy. To support the results of the success rate of telemarketing personnel to market the product in its role of banking services that the process requires a prospective customer data, then data mining support is very important in the classification of the prospective customers of the bank so that it can predict the degree of success in product marketing such services. Based on mapping studies of support data mining on prospective customers to come is no classification algorithms are often used for the classification of a borrower among others Neural Network, Naive Bayes, Dececion Tree, K-NN and Logistic Regreesion, of this algorithm can result from the evaluation process by using Cross Validation, confusion matrix, ROC Curve and T-Test to determine the classification of data mining algo-rithms are the most accurate in predicting success in product mar-keting telemarketing services from the bank to do trials in the Neural Network algorithm was more accurate with an accuracy of 89.71% the AUC value of 0872, this may be a comparison of data mining classi-fication Seeing AUC values of the five methods, then five groups of classification algorithms including both because of its AUC value between 0.80-1.00. Keywords : Comparison of data mining, decision tree, naive Bayes, neural network, k-nn, logistic regression

With the requirement to find methods to analysis the rapidly increasing data, as a branch of computer science, data mining technique has been widely apply in different domain .One of the most enticing application areas of data mining in these emerging technologies is in finance. To complete the financial tasks, data mining technique is used refer to Stock market forecasting, Bankruptcy Prediction, Credit Risk Estimation, Corporate Performance Prediction and Management Fraud. This paper focuses on new data mining applications in finance. The fast development of Compute technology (such as Cloud Computing) and database together gives data mining a new challenge and chance in data mining of finance. How to apply the new computer and database technique to the data mining, and integrate the previous framework and the new technology together to build a more efficiency and safer framework is a long way to go in the future . Meanwhile, after accumulate enough empirical regularity and experiences, deep field-specific theories will be set up. This means, in the future, it will have more standardized and accurate model and method to apply in different domain of finance by generating more empirical regularities and combining them with domain knowledge.

SummaryTo summarize current excellent research in the field of bioinformatics.Synopsis of the articles selected for the IMIA Yearbook 2006. Results: Current research in the field of bioinformatics clearly shows ongoing unification of experimental findings and clinical outcomes. Microarray data, gene sequences and clinical data are more and more perceived as different but related facets of one entity. Significant work is done in the area of text and data mining in order to bring together patient data and biochemical phenomena by means of ontologies. A strong trend in the clinical field is performance of exhaustive studies on DNA material derived from patients that suffer from diseases that are already known to be inherited. Examination of appropriate methods covers data and text mining, ontologies as well as machine learning and classification.The best paper selection of articles on bioinformatics shows examples of excellent research on methods used for studying inherited diseases and their underlying genetic dispositions. Clinical studies, inclusion of experimental findings like microarray data, and of knowledge representation formats all lead to a better understanding the linkage between gene sequences, biological functions and clinical findings in the form of healthy state or physiological disorders.

Presentation delivered at Text and Data Mining Symposium held at the University of Cambridge.

Background: While it is commonly recognized that almost every work and research discipline utilize their own taxonomy, the language used within a specific discipline may also vary depending on numerous factors, including the desired effect of the information being communicated and the intended audience. Different audiences are reached through publication of information, including research results, in different types of publication outlets such as newspapers, newsletters, magazines, websites, and journals. Prior research has shown that students, both undergraduate and graduate, as well as faculty may have a difficult time locating information in different publication outlet types (e.g., magazines, newspapers, journals). The type of publication may affect the ease of understanding and also the confidence placed in the acquired information. A text analytics tool for classifying the source of research as a newsletter (used as a substitute for newspaper articles), a magazine, or an academic journal article has been developed to assist students, faculty, and researchers in identifying the likely source type of information and classifying their own writings with respect to these possible publication outlet types. Literature Review: Literature on information literacy is discussed as this forms the motivation for the reported research. Additionally, prior research on using text mining and text analytics is examined to better understand the methodology employed, including a review of the original Scale of Theoretical and Applied Research system, adapted for the current research. Research Questions: The primary research question is: Can a text mining and text analytics approach accurately determine the most probable publication source type with respect to being from a newsletter, magazine, or journal? Methodology: A text mining and text analytics algorithm, STAR’ (System for Text Analytics-based Ranking), was developed from a previously researched text mining tool, STAR (Scale of Theoretical and Applied Research), that was used to classify the research type of articles between theoretical and applied research. The new text mining method, STAR’, analyzes the language used in manuscripts to determine the type of publication. This method first mines all words from corresponding publication source types to determine a keyword corpus. The corpus is then used in a text analytics process to classify full newsletters, magazine articles, and journal articles with respect to their publication source. All newsletters, magazine articles, and journal articles are from the library and information sciences (LIS) domain. Results: The STAR’ text analytics method was evaluated as a proof of concept on a specific LIS organizational newsletter, as well as articles from a single LIS magazine and a single LIS journal. STAR’ was able to classify the newsletters, magazine articles, and journal articles with 100% accuracy. Random samples from another similar LIS newsletter and a different LIS journal were also evaluated to examine the robustness of the STAR’ method in the initial proof of concept. Following the positive results of the proof of concept, additional journal, magazine, and newsletter articles were used to evaluate the generalizability of STAR’. The second-round results were very positive for differentiating journals and newsletters from other publication types, but revealed potential issues for distinguishing magazine articles from other types of publications. Discussion: STAR’ demonstrates that the language used for transferring information within a specific discipline does differ significantly depending on the intended recipients of the research knowledge. Further work is needed to examine language usage specific to magazine articles. Conclusions: The STAR’ method may be used by students and faculty to identify the likely source of research or discipline-specific information. This may improve trust in the reliability of information due to different levels of rigor applied to different types of publications. Additionally, the STAR’ classifications may be used by students, faculty, or researchers to determine the most appropriate type of outlet and correspondingly the most appropriate type of audience for the reported information in their own manuscripts, thereby improving the chance for successful sharing of information to appropriate audiences who will deem the information to be reliable, through publication in the most relevant outlet type.

It is recognized that text information on Web is growing at an astounding pace. Research and application of text mining on Web is an important branch in the data mining. Now people mainly use information retrieval (IR) or the search engine to look up Web information. But IR focuses on searching for information that is explicitly present but not latent knowledge in some document, the search engine can hardly according to different need of different customers and provide individual service, and it is very difficult to mine data further. However, text mining on Web aims to resolve this problem. This paper discusses an Algorithm of how to follow the appointed website or Web page according to the user's request by using the text mining technique, how to extract and express text characteristic, how to classify the data information with feedback judgement combined with the Web page text contents for later use. We present experiments on different data set that demonstrate more effectiveness of our algorithm than traditional algorithm. The process of Web text mining, information extraction method, mining algorithm and realization technique are discussed in details.