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Contiguous item sequential pattern mining using UpDown Tree

INTELLIGENT data ANALYSIS 2008年第1期12卷 25-49页

作者： Chen, Jinlin CUNY Queens Coll Dept Comp Sci Flushing NY 11367 USA

In this paper the problem of Contiguous Item Sequential Pattern ( CISP) mining is presented as a sequential pattern mining problem under two constraints. First, each element in a sequence consists of only one item. Second, items appearing in the sequences that contain a pattern must be adjacent with respect to the underlying order as they appear in the pattern. Even though the problem of CISP mining can be solved by using previous approaches on sequential pattern mining under a general constraint description framework, this may lead to poor performance due to the large searching space. To efficiently solve this problem, a new data structure, UpDown Tree, is proposed for CISP mining. UpDown Tree based approach can greatly improve the efficiency of CISP mining in terms of both time and memory comparing to previous approaches. An extensive experimental study has shown promising results with our approach.

关键词： data mining algorithm sequential pattern contiguous sequential pattern transaction database sequence database performance analysis

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Digital energy ecosystems

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IFAC-PapersOnLine 2019年第13期52卷 30-35页

作者： Bakhtadze, N. Pavlov, B. Pyatetsky, V. Suleykin, A. V.A. Trapeznikov Institute of Control Sciences Russian Academy of Science 65 Profsoyuznaya Street Moscow117997 Russia National Research Technological University 'MISIS' Moscow Russia

A conceptual approach to the development of a digital ecosystem using multi-agent technology and predictive models is presented with reference to electric power systems. Predictive models are based on data mining algorithms, wavelet analysis and the formation of knowledge bases. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： data mining Ecosystems Electric power systems Multi agent systems Predictive analytics Wavelet analysis Conceptual approaches data mining algorithm Digital ecosystem Knowledge base Knowledge basis Multi agent technologies Predictive models

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Innovative use of health informatics to augment contact tracing during the COVID-19 pandemic in an acute hospital

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2020年第12期27卷 1964-1967页

作者： Venkataraman, Narayan Poon, Beng Hoong Siau, Chuin Changi Gen Hosp Data Management & Informat Singapore Singapore Changi Gen Hosp Resp & Crit Care Med Ambulatory Care Singapore Singapore

This case report describes the innovative design and build of an algorithm that integrates available data from separate hospital-based informatics systems, which perform different daily functions to augment the contact-tracing process of COVID-19 patients by identifying exposed neighboring patients and healthcare workers and assessing their risk. Prior to the establishment of the algorithm, contact-tracing teams comprising 6 members would spend up to 10 hours each to complete contact tracing for 5 new COVID-19 patients. With the augmentation by the algorithm, we observed >= 60% savings in overall man-hours needed for contact tracing when there were 5 or more daily new cases through a time-motion study and Monte Carlo simulation. This improvement to the hospital's contact-tracing process supported more expeditious and comprehensive downstream contact-tracing activities as well as improved manpower utilization in contact tracing.

关键词： COVID-19 contact tracing informatics system data mining algorithm improved manpower utilization

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SKDStream: a dynamic clustering algorithm on time-decaying data stream

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2022年第1期2022卷 102-102页

作者： Liu, Hui Wu, Aihua Wei, Mingkang Chang, Chin-Chen Shanghai Maritime Univ Dept Engn Shanghai Peoples R China Feng Chia Univ Dept Informat Engn & Comp Sci Taichung Taiwan

data stream is a type of data that continue to grow over time. For example, network security data stream will constantly be generated in the field of data security, and encrypted data stream will be generated in the privacy protection scenario. Clustering is a basic task in the analysis of data stream. In addition to the large amount of data and limited computer memory, there are the following challenges in time-decaying data stream clustering: (1) How to quickly process time-varying data stream and how to quickly save vaild data. (2) How to maintain and update clusters and track their evolution in real time. Based on the fact that the existing data stream algorithms do not provide a good strategy to the above problems, this paper proposes a dynamic clustering algorithm named SKDStream. The algorithm divides the entire data space into distinct minimal bound hypercubes, which are maintained and indexed by a newly defined structure, SKDTree, that aggregates and updates clusters in real time without requiring large primary storage. Clusters are composed of dense hypercubes. Experiments on synthetic datasets and real datasets show that the response time of the algorithm is similar to that of existing dataflow algorithms, but the quality of the generated clusters is relatively stable over time. Furthermore, the SKDStream algorithm is able to track the evolution of the number of clusters, centers, and density in real time, and compared to D-stream, SKDStream is efficient and effective in clustering.

关键词： data mining algorithm Density-based clustering Decaying stream High density subspace

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Legal Forensics System Based on data mining

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Procedia Computer Science 2024年 243卷 1089-1094页

作者： Hongrui Dai Law school of Chongqing University Chongqing 400044 China Law school of Yunnan Minzu University Kunming 650504 China

The existing forensics systems are developing in a more and more perfect direction in terms of forensic steps, legal constraints, audit supervision, etc., but they are still not fully applicable to legal forensics. A general legal forensics system must have a complete forensics process to ensure the smooth progress of the forensics work. One of the purposes of legal forensics is to use legal means to bring criminals to justice. Only by ensuring the legality of the evidence can it be accurately identified case, and therefore also to ensure the legality of the evidence collection process. In order to design a more powerful legal forensics system, this paper introduces a data mining algorithm to extract valuable information from massive data through association rules to improve the efficiency of forensics. At the same time, this paper also proposes three coding schemes for the integrity of the evidence object, and compares the forensic effects of the three schemes.

关键词： Legal Forensics data mining algorithm Association Rules Coding Scheme

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Construction of an Intelligent Mental Health Assessment System Based on Big data algorithms

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Procedia Computer Science 2024年 247卷 1102-1109页

作者： Fangrui Men Digital Marketing Industry College Shandong Institute of Commerce and Technology Jinan 250103 Shandong China

College students are in the stage of transitioning from teenagers to adults, and there are many problems that need to be adapted and adjusted in life, learning, and interacting with others. If these issues are not handled properly, it would lead to many mental health problems that affect normal learning and life. The paper use the data mining algorithm of Big data. Through systematic intelligent evaluation, it can gain an overall understanding of the mental status of students. For students, it is effective to have a clear understanding of their mental health status, and to communicate with their home and school in a timely manner regarding their psychological problems. Early treatment of mental health can be more effective in completing rehabilitation; For universities, they can have a clear understanding of each student's psychological situation to avoid some difficult situations. At the same time, universities can timely intervene and treat students with psychological problems. This paper use data mining algorithms to build an intelligent evaluation system for mental health. Through research, it is found that the accuracy of this method is 95% at the lowest and 97% at the highest. This method can comprehensively and objectively describe the changing characteristics of college students’ mental health, and the results of mental health intelligence assessment are stable, providing valuable reference for their mental health management.

关键词： Big data Technology data mining algorithm Mental Health Intelligent Evaluation System

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The Human Body Characteristic Parameters Extraction and Disease Tendency Prediction based on Multi-Sensing Fusion algorithms

The Human Body Characteristic Parameters Extraction and Dise...

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6th lEEE Annual International Conference on Cyber Technalogy in Automation, Control and Intelligent Systems (IEEE-CYBER)

作者： Shi, Guanyi Geng, Chao Liu, Hadiang Su, Hang Jin, Yufeng Sun, Shengli Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Sun Yat Sen Univ Res Inst Shenzhen Peoples R China

ISBN: (纸本)9781509027323

Disease prediction and the pursuit of health has always been the relish topic, especially as the aggravating population aging phenomenon. However, the existing medical knowledge used in disease prediction system is given by experts, which is vulnerable to restrictions of real-time and convenience. On the other hand, people don't want to use the high cost, complex medical monitoring system which could induce the inconvenience of everyday life. At the same time, the design and development of wearable bio-sensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. In this article, we used an electronic health system based on multi-sensor fusion strategy. On the basis of not affecting the daily life, we got a variety of sensors with sensing, computational and data communication features, worn on the body and extracted the targeted health characteristic parameters of human. Therefore, we used the data mining algorithm to process the data in order to predict the disease tendency in long-term. Taking stroke prediction model as an example, after analyzing factors which are related to stroke, we used multi-sensor fusion system and collected 25 healthy attributes, coupled with a class attribute, built the training sample which contains a total of 125 cases of samples with 26 attributes. Based on the training sample, we used machine learning algorithms, where recognition rate could achieve 94.35%. On the basis of the classification weights, we got the most closely attributes associated with stroke and 9 healthy attributes combination with the best classification accuracy of 96.77%. Result shows that the system has achieved good classification accuracy and verified the feasibility of stroke prediction model proposed. Thus, electronic health system based on multi-sensor fusion has a high research value.

关键词： E-health multi-sensor fusion data mining algorithm disease tendency prediction

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IPARBC: An Improved Parallel Association Rule based on MapReduce Framework

IPARBC: An Improved Parallel Association Rule based on MapRe...

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International Conference on Networking and Network Applications (NaNA)

作者： Chang, Hong-Yi Hong, Zih-Huan Lin, Tu-Liang Chang, Wan-Kun Lin, Yi-Ying Natl Chiayi Univ Dept Management Informat Syst Chiayi Taiwan Ind Technol Res Inst Informat & Resource Technol Dept Taichung Taiwan

ISBN: (纸本)9781467398039

Recently, data mining in big data has become an important concern for researchers. data mining, which refers to mining the relationship between items in a dataset, has been applied by businesses to seek profitable outcomes. Association rule mining algorithms such as Apriori and the FP-growth are efficient methods for discovering relations between items in large databases. To enhance the performance, many researches tend to enhance the traditional method using the MapReduce framework. In this paper, we proposed an improved association rule algorithm (IPARBC) based on MapReduce framework. The concept of combinatorial mathematics is used as the theoretical basis of the algorithm, and in order to improve mining performance by MapReduce framework, we address the high volume problem of big data. Experimental results show that the proposed algorithm outperform other algorithms substantially in terms of runtime.

关键词： data mining algorithm Big data MapReduce Framework Cloud Computing

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PREDICTING CUSTOMER DEMOGRAPHICS IN A MOBILE SOCIAL NETWORK

PREDICTING CUSTOMER DEMOGRAPHICS IN A MOBILE SOCIAL NETWORK

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International Conference on Advances in Social Networks Analysis and mining (ASONAM)

作者： Aarthi, S. Bharanidharan, S. Saravanan, M. Anand, V Sri Venkateswara Coll Engn Dept Comp Sci & Engn Madras Tamil Nadu India Ericsson India Pvt Ltd Ericsson R&D Madras Tamil Nadu India

ISBN: (纸本)9780769543758

A network made of individuals connected based on their communication behaviour using mobile phones can be called as a Mobile Social Network. Mapping and measuring of interactions and flows between people across mobile social networks are being performed extensively in an attempt to understand the intriguing patterns of human behaviour. Such analyses can help in arriving at useful inferences for improving the accuracy of 'Targeted Advertisements'. This paper makes one such attempt to extract the demographics (i.e gender and economic status) of a person based on his/her connectivity in his/ her respective social network(s) and mobile phone usage over a period of time. The need for prediction arises from the fact that, for prepaid users, the demographics are either unavailable or inaccurate. The results produced are evaluated and standardized based on proven statistics pertaining to the nation considered. We use candlestick charts to compare the experimental results.

关键词： data mining algorithm data mining Demographics Prediction Economics Mobile Social Network Analysis Mobile communication Mobile computing Mobile handsets Social network services Targeted Advertisement Telecommunication data Training advertising behavioural sciences computing communication behaviour customer demographics human behaviour mobile computing mobile phone usage mobile social networks social networking (online) targeted advertisement

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Using Different Substitution Matrices in a String-Matching Technique for Identifying Viral Polymorphic Malware Variants

Using Different Substitution Matrices in a String-Matching T...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Naidu, Vijay Narayanan, Ajit Auckland Univ Technol Sch Comp & Math Sci Auckland New Zealand

ISBN: (纸本)9781509006229

Antivirus systems have difficulty in detecting polymorphic variants of known viruses without explicit signatures for such variants. Initial work on investigating efficient and effective string-based approaches for the automatic generation of signatures for the identification of some or all new polymorphic variants, was initially encouraging. That initial work was restricted by a number of experimental aspects. The aim of the research reported here is to examine the effects of using different substitution matrices in a string-based methods for the automatic generation of signatures for the detection of some or all new polymorphic variants. We establish how our proposed syntactic-based method using the well-known string matching Smith-Waterman algorithm can successfully identify the known polymorphic variants of *** virus. Our string-matching technique may metamorphose our understanding of polymorphic variant generation and may lead to a new phase of syntactic-based anti-viral software.

关键词： string matching algorithm Smith-Waterman algorithm JS.Cassandra virus local pairwise sequence alignment substitution matrix multiple sequence alignment data mining algorithm automatic signature generation

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