Pattern Discovery Techniques Research Articles

Survival Analysis of Oncological Patients Using Machine Learning Method

Currently, a considerable volume of information is collected and stored by large health institutions. These data come from medical records and hospital records, and the Hospital Cancer Registry is a database for integrating data from hospitals throughout Iraq. The data mining (DM) technique provides knowledge previously not visible in the database and can be used to predict trends or describe characteristics of the past. DM methods can include classification, generalisation, characterisation, clustering, association, evolution, pattern discovery, data visualisation, and rule-guided mining techniques to perform survival analyses that take into account all the patient's medical record variables. For four of the eleven groups examined, this accuracy was relatively high. The database of patients treated by the Baghdad Teaching Hospital between 2018 and 2021 was examined using a classification of the most crucial variables for event prediction, and a distinctive pattern was found. Machine learning techniques allow a global assessment of the data that is available and produce results that can be interpreted as significant information for epidemiological studies, even in cases where the sample is small and there is a lack of information on several variables.

Multiple educational data mining approaches to discover patterns in university admissions for program prediction

<span>This paper presented the utilization of pattern discovery techniques by using multiple relationships and clustering educational data mining approaches to establish a knowledge base that will aid in the prediction of ideal college program selection and enrollment forecasting for incoming freshmen. Results show a significant level of accuracy in predicting college programs for students by mining two years of student college admission and graduation final grade scholastic records. The results of educational predictive data mining methods can be applied in improving the services of the admission department of an educational institution, particularly in its course alignment, student mentoring, admission forecast, marketing, and enrollment preparedness.</span>

Regression Tree Based Correlation Technique in Spatial Data Classification

Data mining is the process of discovering useful patterns from large geo-spatial datasets with the help of machine learning methods. . The machine learning methods plays an important role for data analytics modeling and visualization. Geo-spatial data is a significant task in many application domains, such as environmental science, geographic information science, and social networks. However, the existing spatial pattern discovery and prediction techniques failed to predict the event accurately with minimum error and time consumption. . In this paper, a novel Pearson Correlated Regression Tree-based Affine Projective spatial data Classification (PCRT-APSDC) technique is proposed to improve the spatial data classification and minimize error based on the Affine Projective classification technique. The proposed algorithm employs a fuzzy rule procedure that constructs the regression tree. The fuzzy rule is applied for linking the inputs (i.e. spatial data) with the outputs (i.e. classification results). Our goal is to classify the data into two subsets such as fired region and non-fired region. Experimental evaluation is carried out using a forest fire dataset with different factors such as classification accuracy, false-positive rate, and classification time. The results confirm that the proposed technique predicts the fired region with increased spatial data classification accuracy and minimized time as well as false-positive rate than the state-of-the-art methods.

Pattern discovery, validation, and online experiments: a methodology for discovering television shows for public health announcements.

Public Health Announcements (PHAs) on television are a means of raising awareness about risk behaviors and chronic conditions. PHAs' scarce airtime puts stress on their target audience reach. We seek to help health campaigns select television shows for their PHAs about smoking, binge drinking, drug overdose, obesity, diabetes, STDs, and other conditions using available statistics. Using Nielsen's TV viewership database for the entire US panel, we presented a novel show discovery methodology for PHAs that combined (i) pattern discovery from high-dimensional data (ii) nonparametric tests for validation, and (iii) online experiments on Facebook. The nonparametric tests verified the robustness of the discovered associations between the popularity of certain shows and health conditions. Findings from fifty (independent) online experiments (where our awareness messages were seen by nearly 1.5 million American adults) empirically demonstrated the value of the methodology. For 2016, the methodology identified several shows whose popularities were genuinely associated with certain health conditions, opening up the possibility of health agencies embracing both big data and large-scale experimentation to address an old problem in a new way. Policy makers can repeatedly apply the methodology as new data streams in, with perhaps different feature sets, pattern discovery techniques, and online experiments running over longer periods. The comparatively lower initial investment in the methodology can pay off by identifying several shows for a potentially national television campaign. As simply a by-product, the initial investment also results in awareness messages that might reach millions of individuals.

Mobility pattern analysis of ship trajectories based on semantic transformation and topic model

Recognition and understanding of ship mobility pattern have great significance for intelligent maritime applications, i.e. route discovery and anomaly detection. Besides a number of pattern discovery techniques currently derived from ship trajectory, topic modeling popular in the field of Natural Language Processing may provide a novel way to detect implicit patters underlying massive ship trajectories treated as documents. This paper is motivated to apply a semantic analysis method to explore potential mobility patterns from ship trajectories in inland river by combining semantic transformation and topic model. A coarse-grained semantic transformation model is firstly defined to translate each ship trajectory into a document containing a series of sequential motion words. A motion word is generally a synthetic semantic representation of three trajectory features (location, course and speed). All ship trajectories can then be examined and analyzed as a document corpus. A classic topic model (i.e. Latent Dirichlet Allocation, LDA) is employed to explore hidden ship mobility patterns from trajectory document corpus. The effectiveness of this approach is illustrated through a case study at Wuhan waterway, located at middle stream of Yangtze River in China.

Employing geometric pattern discovery techniques for sonar target recognition

Automated sonar target recognition suffers from linear superposition of acoustic color features, which need to be precisely disambiguated to correctly identify a target of the specific composition and geometry. Most target acoustic color images exhibit feature overlap between environmental reverberation as well as reflections due to target geometry, and elastic waves that are representative of the material composition of the target. From a signal processing perspective, this is essentially mitigating and disentangling interference from two highly competing yet hard-to-predict sources: reflections from the environment and reflections due to the target geometry, which represent different physical phenomena with distinct geometric patterns. We will present recent results in feature selection and interference mitigation employing Gabor wavelets to separate target acoustic color features due to elastic wave orbits from interfering features due to the target geometry and environmental effects. We will provide results from simulations as well as experimental field data and discuss the trade-offs in feature representations employing acoustic color and those that also include the phase information. Finally, we will discuss how this type of feature extraction can be used to discover and create robust priors for Bayesian inference networks for learning salient target features.

A fuzzy hierarchy-based pattern matching technique for melody classification

Classification of classical melodic structures by style, composer, genre, period, etc., is a rather complex task. The level of difficulty varies across melodic frameworks. It would be interesting to see how we can impart this ability to a machine. This paper reports an improved pattern matching technique for composer and raga classification using a fuzzy analytical hierarchy process-based approach. The technique makes use of class-specific patterns extracted from a pattern discovery technique known as Structure Induction Algorithm for r superdiagonals and compactness trawler. Further, to represent inexact matches a modified matching technique is proposed to assign weights to the exact matching scores in a probabilistic manner. Subsequently, the weighted scores are fuzzified to quantify the extent of match. Finally, the fuzzy scores are aggregated and classified on the basis of minimum Euclidean distance from an ideal solution in the pattern space. Experiments conducted on datasets containing a wide range of melodies from classical western and classical Indian background show that the proposed technique exhibits consistently better classification success rate compared to the exact n-Gram-based approach and a widely used matching algorithm based on Levenshtein distance.

Mining Negatives Association Rules Using Constraints

Pattern discovery techniques, such as association rule discovery is one of the fundamental problem in data mining. Usually the task is limited to positive rules of the form X → Y when X and Y are susbets of items. To enlarge the knowledge discovery from data. Many works pointed out that other rules can be mined linking the present items in transactions with the missing ones designed as negative rules. To mine the most relevant negative rules, the mining task of negative association rules is often coupled with new measure such as lift or conviction to limit the set of extracted association rules.In this work we address the problem of mining strong negative rules by extending the SAT-Based approach proposed in [1]. We show that the conviction constraint leads to a non-linear constraints that have to be managed efficiently to prune the search space. Experiments results explore the efficiency of our new approach.

A data mining framework for environmental and geo-spatial data analysis

Mining geo-spatial data is an important task in many application domains, such as environmental science, geographic information science, and social networks. In this paper, we introduce a data mining framework, which includes pre-processing of environmental and geo-spatial data, geo-spatial data mining techniques, and visual analysis of environmental and geo-spatial data. In particular, we propose new density-based clustering algorithms to identify interesting distribution patterns from geo-spatial data, a change pattern discovery technique to detect dynamic change patterns within spatial clusters, and a post-processing technique to extract interesting patterns and useful knowledge from geo-spatial data. Our density-based clustering algorithms are based on the well-established density-based shared nearest neighbor clustering algorithm, which can find clusters of different shape, size, and densities in high-dimensional data. The post-processing analysis technique allows automatic screening of interesting spatial clusters. The change pattern discovery algorithm is able to detect and analyze dynamic patterns of changes within spatial clusters. This paper focuses on developing a framework integrating a sequence of data mining process including clustering algorithm, analysis technique and pattern changing discovery algorithm. In contrast to previous works in this area, our approaches can cluster and analyze dynamically evolved complex objects, i.e., polygons. We evaluate the effectiveness of our techniques through a challenging real case study involving ozone pollution events in the Houston–Galveston–Brazoria area. The experimental results show that our approaches can discover interesting patterns and useful information from geo-spatial air-quality data.

Classification of auditory brainstem responses through symbolic pattern discovery.

Numeric time series are present in a very wide range of domains, including many branches of medicine. Data mining techniques have proved to be useful for knowledge discovery in this type of data and for supporting decision-making processes. The overall objective is to classify time series based on the discovery of frequent patterns. These patterns will be discovered in symbolic sequences obtained from the time series data by means of a temporal abstraction process. Firstly, we transform numeric time series into symbolic time sequences, where the symbols aim to represent the relevant domain concepts. These symbols can be defined using either public or expert domain knowledge. Then we apply a symbolic pattern discovery technique to the output symbolic sequences. This technique identifies the subsequences frequently found in a population group. These subsequences (patterns) are representative of population groups. Finally, we employ a classification technique based on the identified patterns in order to classify new individuals. Thanks to the inclusion of domain knowledge, the classification results can be explained using domain terminology. This makes the results easier to interpret for the domain specialist (physician). This method has been applied to brainstem auditory evoked potentials (BAEPs) time series. Preliminary experiments were carried out to analyse several aspects of the method including the best configuration of the pattern discovery technique parameters. We then applied the method to the BAEPs of 83 individuals belonging to four classes (healthy, conductive hearing loss, vestibular schwannoma-brainstem involvement and vestibular schwannoma-8th-nerve involvement). According to the results of the cross-validation, overall accuracy was 99.4%, sensitivity (recall) was 97.6% and specificity was 100% (no false positives). The proposed method effectively reduces dimensionality. Additionally, if the symbolic transformation includes the right domain knowledge, the method arguably outputs a data representation that denotes the relevant domain concepts more clearly. The method is capable of finding patterns in BAEPs time series and is very accurate at correctly predicting whether or not new patients have an auditory-related disorder.

Attack‐pattern discovery based enhanced trust model for secure routing in mobile ad‐hoc networks

SummaryMulti‐hop routing in an open environment in the absence of well‐established infrastructure and centralized authority strives for trustworthiness and cooperation of nodes in a Mobile Ad‐hoc Network (MANET). There is no guarantee of secure and reliable delivery of packets when some internal nodes intentionally perform packet forwarding misbehavior by compromising the routing mechanism. In this paper, we address this issue with a trust‐model integrated with an attack pattern discovery technique. Extended from the Ad‐hoc On‐demand Distance Vector (AODV) routing protocol, we propose a trust‐based scheme founded on nodes' historical behaviors which adopts a pattern discovery mechanism in order to detect suspicious activities from the malevolent nodes before they start dropping data packets. We also present the detailed mode of operations of three distinct adversary models launching various kinds of packet forwarding misbehavior. Theoretical analysis and experimental results prove that the integration of a pattern discovery method with a trust‐based model provides earlier detection of adversaries which follow specific attack patterns and weakens their damaging effects on the network in comparison to a solitary trust‐based model. Copyright © 2016 John Wiley & Sons, Ltd.

A NOVEL APPROACH FOR TEXT EXTRACTION USING EFFECTIVE PATTERN MATCHING TECHNIQUE

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.

Classification and comparison of architecture evolution reuse knowledge—a systematic review

ABSTRACTContextArchitecture‐centric software evolution (ACSE) enables changes in system's structure and behaviour while maintaining a global view of the software to address evolution‐centric trade‐offs. The existing research and practices for ACSE primarily focus on design‐time evolution and runtime adaptations to accommodate changing requirements in existing architectures.ObjectivesWe aim to identify, taxonomically classify and systematically compare the existing research focused on enabling or enhancing change reuse to support ACSE.MethodWe conducted a systematic literature review of 32 qualitatively selected studies and taxonomically classified these studies based on solutions that enable (i) empirical acquisition and (ii) systematic application of architecture evolution reuse knowledge (AERK) to guide ACSE.ResultsWe identified six distinct research themes that support acquisition and application of AERK. We investigated (i) how evolution reuse knowledge is defined, classified and represented in the existing research to support ACSE and (ii) what are the existing methods, techniques and solutions to support empirical acquisition and systematic application of AERK.ConclusionsChange patterns (34% of selected studies) represent a predominant solution, followed by evolution styles (25%) and adaptation strategies and policies (22%) to enable application of reuse knowledge. Empirical methods for acquisition of reuse knowledge represent 19% including pattern discovery, configuration analysis, evolution and maintenance prediction techniques (approximately 6% each). A lack of focus on empirical acquisition of reuse knowledge suggests the need of solutions with architecture change mining as a complementary and integrated phase for architecture change execution. Copyright © 2014 John Wiley & Sons, Ltd.

USING DATA MINING METHODS KNOWLEDGE DISCOVERY FOR TEXT MINING

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open researc h issue, especially in the domain of text mining. S ince most existing text mining methods adopted term-based approaches, they all suf fer from the problems of polysemy and synonymy. Ove r the years, people have often held the hypothesis that pattern (or phrase)- based approaches should perform better than the ter m-based ones, but many experiments do not support this hypothesis. Propose d work presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pat tern evolving, to improve the effectiveness of usin g and updating discovered patterns for finding relevant and interesting infor mation.

Role of Computers in Bioinformatics by Using Different Biological Datasets

The amount and variety of data in natural sciences increases rapidly. Data abstraction, data manipulation and pattern discovery techniques are of great need in order to deal with such large quantities. Integration between different sources of data is also of major interest, as complex relations may arise. Biology is a good example of a field that provides extensive, highly variable and multi-sources data. The scope of these investigations has now expanded greatly owing to the development of high throughout sequencing techniques and novel statistical and computational methods. These methods require easy-to-use computer programs. We have developed a new software package, molecular genetics analysis to integrate their diverse data and tools under Common Graphical User Interfaces (GUIs) by using BLAST, FASTA data searches. One of the most exciting things about being involved in computer programming and biology is that both fields are rich in new techniques and results. The illumina is sequence analyzer in Next Generation Sequence Technology.

A dynamic replica recognization using lsi algorithm

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This project presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. For efficient pattern mining, Latent Semantic Indexing (LSI) is used, a new approach to automatic indexing and retrieval. It is designed to overcome a fundamental problem that plagues existing retrieval techniques that try to match words of queries with words of documents. The particular “latent semantic indexing” (LSI) analysis that we have tried uses singular-value decomposition. We take a large matrix of term-document association data and construct a “semantic” space wherein terms and documents that are closely associated are placed near one another. Singular-value decomposition allows the arrangement of the space to reflect the major associative patterns in the data, and ignore the smaller, less important influences. As a result, terms that did not actually appear in a document may still end up close to the document, if that is consistent with the major patterns of association in the data. Position in the space then serves as the new kind of semantic indexing, and retrieval proceeds by using the terms in a query to identify a point in the space, and documents in its neighborhood are returned to the user.

A SAX-GA approach to evolve investment strategies on financial markets based on pattern discovery techniques

This paper presents a new computational finance approach, combining a Symbolic Aggregate approXimation (SAX) technique together with an optimization kernel based on genetic algorithms (GA). The SAX representation is used to describe the financial time series, so that, relevant patterns can be efficiently identified. The evolutionary optimization kernel is here used to identify the most relevant patterns and generate investment rules. The proposed approach was tested using real data from S&P500. The achieved results show that the proposed approach outperforms both B&H and other state-of-the-art solutions.

Preventive Dynamic Security Control of Power Systems Based on Pattern Discovery Technique

This paper presents a statistical learning-based method for preventive dynamic security control of power systems. Critical operating variables regarding system dynamic security are first selected via a distance-based feature estimation process. An unsupervised learning procedure called pattern discovery (PD) is then performed in the space of the critical variables to extract the subtle structure knowledge called patterns. The patterns are geometrically non-overlapped hyper-rectangles, representing the system dynamic secure/insecure regions and can be explicitly presented to provide decision support for real-time security monitoring and situational awareness. By formulating the secure patterns into a standard optimal power flow (OPF) model, the preventive control against dynamic insecurities can be efficiently and transparently attained. The proposed method is validated on the New England 39-bus system considering both single- and multi-contingency conditions.

FRAMEWORK FOR WEB PERSONALIZATION USING WEB MINING

WWW is a large amount of information provider and a very big source of information. Users are increasing every day for accessing web sites. For efficient and effective handling, web mining coupled with suggestion techniques provides personalized contents at the disposal of users. Web Mining is an area of Data Mining dealing with the extraction of interesting knowledge from the Web. Here we are presenting a comprehensive overview of the personalization process based on Web usage mining. In this a host of Web usage mining activities required for this process, including the pre-processing and integration of data from multiple sources, and common pattern discovery techniques that are applied to the integrated usage data.

Discovery of Patterns to Improve Usability of Electronic Health Record Systems

A majority of research efforts in the domain of Electronic Health Records concentrate on standardization and related issues. The earlier forms of medical records did not permit a high level of exchange, interoperability or extensive search and querying. Recent research has focused on the development of open standards for life-time long health record archives for individual patients. This facilitates the extensive use of data mining and querying techniques for the analysis. These efforts can increase the depth and the extent of the utilization of patient data. For example, association analysis can be used to identify common features among disparate patients to check whether diagnoses or procedures are effective. Pattern discovery techniques can also be used to create the census reports and generate a meaningful visualization of summary data at hospitals. For handling the large volume of data, there is a need to focus on improving the usability. The current study proposes a model for the development of EHR support systems. It aims to capture the health worker's needs in a scientific way, on a continuous basis. The proposal has been evaluated for the accuracy of knowledge discovery to improve the usability.