Text data mining: discovery of important keywords in the cyberspace

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.

Techniques in Computational Geometry

Topological sweep of the complete graph

Well known estimation techniques in computational geometry usually deal only with single geometric entities as unknown parameters and do not account for constrained observations within the estimation.The estimation model proposed in this paper is much more general, as it can handle multiple homogeneous vectors as well as multiple constraints. Furthermore, it allows the consistent handling of arbitrary covariance matrices for the observed and the estimated entities. The major novelty is the proper handling of singular observation covariance matrices made possible by additional constraints within the estimation. These properties are of special interest for instance in the calculus of algebraic projective geometry, where singular covariance matrices arise naturally from the non-minimal parameterizations of the entities.The validity of the proposed adjustment model will be demonstrated by the estimation of a fundamental matrix from synthetic data and compared to heteroscedastic regression [1], which is considered as state-of-the-art estimator for this task. As the latter is unable to simultaneously estimate multiple entities, we will also demonstrate the usefulness and the feasibility of our approach by the constrained estimation of three vanishing points from observed uncertain image line segments.KeywordsMahalanobis DistanceFundamental MatrixStraight Line SegmentHomogeneous EntityRandom Sample ConsensusThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Computational algebraic geometry has experienced significant advancements in recent years, driven by both theoretical breakthroughs and practical applications comprehensive review of the latest techniques in computational algebraic geometry, highlighting their development and impact on various domains. We begin by discussing foundational methods, including Gröbner bases, resultants, and elimination theory, which have been pivotal in solving polynomial systems and understanding algebraic varieties emerging algorithms that leverage improvements in computational efficiency, such as homotropy continuation methods and numerical algebraic geometry techniques. the applications of these advancements across different fields. In robotics and computer vision, for example, algebraic geometry methods are applied to solve problems related to motion planning and object recognition. In cryptography, recent developments in computational algebraic geometry offer new approaches to designing secure cryptographic systems resistant to quantum attacks. We also discuss the integration of algebraic geometry with machine learning and data science, emphasizing its role in improving predictive modeling and pattern recognition.

Gigantic amount of content is generated every second on the web. Blog posts, tweets, videos, uploaded images maintain the rich and increasing content of the social media. Great amount of information is obtained from social media through text mining techniques which are being improved and developed continuously. Due to the increasing variety of social media, data is flooding into every business and techniques of gathering information from unstructured data becomes a challenge. Processing and analyzing of large collections of social media text is difficult by using traditional single computer methods. Fortunately, with each passing day, the development and improvements in Big Data infrastructure are promising and the analysis capabilities are also increasing. This infrastructure supports language independent researches on text mining. There can be diversified searches with language-specific definitions and assumptions. Since many of the studies on social media text mining analysis focus on English language, this paper would help enhance text mining efficiently in Turkish language which is a word order free agglutinative language. The linguistic aspects of Turkish language make text mining complicated. Suffixes can change the word meanings easily. Word stemming becomes elaborated due to indigenous letters. The intent of this article is to demonstrate the issues, methods and applications of qualitative approaches that enable text mining in Turkish language considering its compelling features with covering preprocessing, feature selection and topic clustering on text mining considering Turkish linguistic features.

Maze solving has direct applications like Robot path finding, emergency evacuation, and drug discovery. Traditional methods of maze solving treat the problem of maze as a directional graph by connecting the key nodes. Because of the directional graph treatment, which is applied on the simplified form of the original maze, the maze solving algorithms at present cannot be directly applied in the critical domains of emergency evacuation etc. The proposed method uses the original complex maze without simplifying and hence can be directly applied in any critical application related to path finding. Proposed approach solves the mazes in a fully automated mode by combining the techniques of image processing, computational geometry and computational fluid dynamics. Moreover the high amount of human intervention needed for generating graphs from mazes in the traditional algorithms is eliminated altogether.

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.

Tangible cultural heritage has provided visually appealing material for those researching in computer graphics, computational geometry, and interactive techniques. However, experiments were initially of more benefit to computer scientists as tests for their novel techniques than of any practical benefit to cultural-heritage professionals. Recently, however, tools have matured considerably, and technology is now enabling novel applications for documenting and preserving tangible cultural heritage and is continuing to inspire computer graphics researchers. Nevertheless, there's still a long way to go.

In this paper, we propose a novel FPGA-based architecture for large-scale regular expression matching, called dynamic reconfigurable bit-parallel NFA architecture (Dynamic BP-NFA) that allows dynamic reconfiguration of the patterns using bit-parallel NFA-simulation. This is the first dynamic reconfigurable FPGA-based hardware with guaranteed performance for the class of extended patterns, where an extended pattern is a restricted regular expression in linear form consisting of letters, classes of letters, don't cares, optional letters, bounded and unbounded length gaps and repeatable letters. The key to our architecture is the use of bit-parallel pattern matching approach that has been developed in string matching communities for the decades. In this approach, the information of an input NFA is compactly encoded in bit-masks stored in a collection of registers and block RAMs. Then, the NFA is efficiently simulated by a fixed circuitry using a combination of bit- and arithmetic-operations on these bit-masks consuming one input letter per clock. As compared with the previous approaches of DFA-based dynamic reconfigurable architectures, experimental results show that the proposed architecture achieves higher throughput for the class of exact string patterns and comparable for the class of extended patterns.

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%.

We explain the origin of the Veronese surface in the vacuum moduli space geometry of the MSSM electroweak sector. While this result appeared many years ago using techniques of computational algebraic geometry, it has never been demonstrated analytically. Here, we present an analytical derivation of the vacuum geometry of the electroweak theory by understanding how the F- and D-term relations lead to the Veronese surface. We moreover give a detailed description of this geometry, realising an extra branch as a zero-dimensional point when quadratic Higgs lifting deformations are incorporated into the superpotential.

String Matching is a technique of searching a pattern in a text. It is the basic concept to extract the fruitful information from large volume of text, which is used in different applications like text processing, information retrieval, text mining, pattern recognition, DNA sequencing and data cleaning etc., . Though it is stated some of the simple mechanisms perform very well in practice, plenty of research has been published on the subject and research is still active in this area and there are ample opportunities to develop new techniques. For this purpose, this paper has proposed linear array based string matching, string matching with butterfly model and string matching with divide and conquer models for sequential and parallel environments. To assess the efficiency of the proposed models, the genome sequences of different sizes (10–100 Mb) are taken as input data set. The experimental results have shown that the proposed string matching algorithms performs very well compared to those of Brute force, KMP and Boyer moore string matching algorithms.

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.

In this paper, we implement recent theoretical progress of depth-first algorithms for mining flock patterns (Arimura et al., 2013) based on depth-first frequent itemset mining approach, such as Eclat (Zaki, 2000) or LCM (Uno et al., 2004). Flock patterns are a class of spatio-temporal patterns that represent a groups of moving objects close each other in a given time segment (Gudmundsson and van Kreveld, Proc. ACM GIS’06; Benkert, Gudmundsson, Hubner, Wolle, Computational Geometry, 41:11, 2008). We implemented two extensions of a basic algorithm, one for a class of closed patterns, called rightward length-maximal flock patterns, and the other with a speed-up technique using geometric indexes. To evalute these extensions, we ran experiments on synthesis datasets. The experiments demonstrate that the modified algorithms with the above extensions are several order of magnitude faster than the original algorithm in most parameter settings.