The Application of Data Mining in the Honeypot System

Sequential pattern mining methods have been found to be applicable in a large number of domains. Sequential data is omnipresent. Sequential pattern mining methods have been used to analyze this data and identify patterns. Such patterns have been used to implement efficient systems that can recommend based on previously observed patterns, help in making predictions, improve usability of systems, detect events, and in general help in making strategic product decisions. In this chapter, we discuss the applications of sequential data mining in a variety of domains like healthcare, education, Web usage mining, text mining, bioinformatics, telecommunications, intrusion detection, et cetera. We conclude with a summary of the work.

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Pattern Based Feature Construction in Semantic Data Mining

International Journal on Semantic Web and Information Systems ◽

10.4018/ijswis.2014010102 ◽

2014 ◽

Vol 10 (1) ◽

pp. 27-65 ◽

Cited By ~ 11

Author(s):

Agnieszka Ławrynowicz ◽

Jędrzej Potoniec

Keyword(s):

Data Mining ◽

Pattern Mining ◽

Semantic Features ◽

Semantic Data ◽

Data Mining Approach ◽

Meta Learning ◽

New Type ◽

Domain Ontologies ◽

Semantic Data Mining

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

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Multi-Relational Sequential Pattern Mining Based on Iceberg Concept Lattice

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.109.729 ◽

2011 ◽

Vol 109 ◽

pp. 729-733

Author(s):

Jiang Yin ◽

Yun Li ◽

Cen Cheng Shen ◽

Bo Liu

Keyword(s):

Data Mining ◽

Time Complexity ◽

Pattern Mining ◽

Concept Lattice ◽

Optimization Methods ◽

Sequential Pattern Mining ◽

Experimental Results ◽

Sequential Pattern ◽

Sequential Mining ◽

Mining Methods

Multi-Relational Sequential mining is one of the areas of data mining that rapidly developed in recent years. However, the performance issues of traditional mining methods are not ideal. To effectively mining the pattern, we proposed an algorithm based on Iceberg concept lattice, adopting optimization methods of partition and merger to just mining the frequent sequences. Experimental results show this algorithm effectively reduced the time complexity of multi-relational sequential pattern mining.

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Apriori-based High Efficiency Load Balancing Parallel Data Mining Algorithms on Multi-core Architectures

International Journal of Grid and High Performance Computing ◽

10.4018/ijghpc.2015040106 ◽

2015 ◽

Vol 7 (2) ◽

pp. 77-99 ◽

Cited By ~ 4

Author(s):

Kun-Ming Yu ◽

Sheng-Hui Liu ◽

Li-Wei Zhou ◽

Shu-Hao Wu

Keyword(s):

Data Mining ◽

Load Balancing ◽

Pattern Mining ◽

High Efficiency ◽

Computation Time ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

Parallel Data ◽

Parallel Data Mining ◽

Mining Methods

Frequent pattern mining has been playing an essential role in knowledge discovery and data mining tasks that try to find usable patterns from databases. Efficiency is especially crucial for an algorithm in order to find frequent itemsets from a large database. Numerous methods have been proposed to solve this problem, such as Apriori and FP-growth. These are regarded as fundamental frequent pattern mining methods. In addition, parallel computing architectures, such as an on-cloud platform, a grid system, multi-core and GPU platform, have been popular in data mining. However, most of the algorithms have been proposed without considering the prevalent multi-core architectures. In this study, multi-core architectures were used as well as two high efficiency load balancing parallel data mining methods based on the Apriori algorithm. The main goal of the proposed algorithms was to reduce the massive number of duplicate candidates generated using previous methods. This goal was achieved for, in this detailed experimental study the algorithms performed better than the previous methods. The experimental results demonstrated that the proposed algorithms had dramatically reduced computation time when using more threads. Moreover, the observations showed that the workload was equally balanced among the computing units.

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The Use of Data Mining Methods for the Prediction of Dementia: Evidence From the English Longitudinal Study of Aging

IEEE Journal of Biomedical and Health Informatics ◽

10.1109/jbhi.2019.2921418 ◽

2020 ◽

Vol 24 (2) ◽

pp. 345-353 ◽

Cited By ~ 2

Author(s):

Hui Yang ◽

Peter A. Bath

Keyword(s):

Data Mining ◽

Longitudinal Study ◽

Use Of Data ◽

Mining Methods ◽

English Longitudinal Study

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The use of Data Mining methods to assess the reliability of bank lending borrowers

Voprosy regionalnoj ekonomiki ◽

10.21499/2078-4023-2017-31-2-108-112 ◽

2017 ◽

Vol 31 (2) ◽

pp. 108-112

Author(s):

N. P. Sidorova

Keyword(s):

Data Mining ◽

Bank Lending ◽

Scoring Method ◽

Credit Risks ◽

Use Of Data ◽

Mining Methods ◽

Banking Risk

Estimates of credit banking risk is one of the topical tasks of banking. Correct and timely assessment of the reliability of the bank's customers who applied for the loan will help reduce the bank's losses associated with credit risks. To increase the efficiency and validity of making decisions on the issuance of a loan, Data Mining methods can be used. The article considers Data Mining technologies, which are applicable for the implementation of the scoring method of the borrower's assessment.

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Pattern Based Feature Construction in Semantic Data Mining

Mobile Computing and Wireless Networks ◽

10.4018/978-1-4666-8751-6.ch036 ◽

2016 ◽

pp. 823-864

Author(s):

Agnieszka Ławrynowicz ◽

Jędrzej Potoniec

Keyword(s):

Data Mining ◽

Pattern Mining ◽

Semantic Features ◽

Semantic Data ◽

Data Mining Approach ◽

Meta Learning ◽

New Type ◽

Domain Ontologies ◽

Semantic Data Mining

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

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Use of data mining methods in planning and management at Russian wood enterprises

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/806/1/012038 ◽

2021 ◽

Vol 806 (1) ◽

pp. 012038

Author(s):

I A Zakharenkova ◽

T P Belyaeva ◽

I N Igotti ◽

T O Terenteva

Keyword(s):

Data Mining ◽

Use Of Data ◽

Planning And Management ◽

Mining Methods

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The Use of Data Mining Methods to Predict the Result of Infertility Treatment Using the IVF ET Method

Studies in Logic, Grammar and Rhetoric ◽

10.2478/slgr-2014-0044 ◽

2014 ◽

Vol 39 (1) ◽

pp. 67-74 ◽

Cited By ~ 2

Author(s):

Paweł Malinowski ◽

Robert Milewski ◽

Piotr Ziniewicz ◽

Anna Justyna Milewska ◽

Jan Czerniecki ◽

...

Keyword(s):

Data Mining ◽

Treatment Process ◽

Infertility Treatment ◽

New Methods ◽

Use Of Data ◽

Treat Ment ◽

Modern Methods ◽

Mining Methods ◽

Effective Analysis ◽

Ivf Et

Abstract The IVF ET method is a scientifically recognized infertility treat- ment method. The problem, however, is this method’s unsatisfactory efficiency. This calls for a more thorough analysis of the information available in the treat- ment process, in order to detect the factors that have an effect on the results, as well as to effectively predict result of treatment. Classical statistical methods have proven to be inadequate in this issue. Only the use of modern methods of data mining gives hope for a more effective analysis of the collected data. This work provides an overview of the new methods used for the analysis of data on infertility treatment, and formulates a proposal for further directions for research into increasing the efficiency of the predicted result of the treatment process.

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