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Literatura académica sobre el tema "Association rule mining. Fuzzy sets"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 2 de febrero de 2022

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Artículos de revistas sobre el tema "Association rule mining. Fuzzy sets"

Matthews, Stephen G., Mario A. Gongora y Adrian A. Hopgood. "Evolutionary algorithms and fuzzy sets for discovering temporal rules". International Journal of Applied Mathematics and Computer Science 23, n.º 4 (1 de diciembre de 2013): 855–68. http://dx.doi.org/10.2478/amcs-2013-0064.

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Abstract A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method’s ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.

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Khan, M. Sulaiman, Maybin Muyeba, Frans Coenen, David Reid y Hissam Tawfik. "Finding Associations in Composite Data Sets". International Journal of Data Warehousing and Mining 7, n.º 3 (julio de 2011): 1–29. http://dx.doi.org/10.4018/jdwm.2011070101.

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In this paper, a composite fuzzy association rule mining mechanism (CFARM), directed at identifying patterns in datasets comprised of composite attributes, is described. Composite attributes are defined as attributes that can take simultaneously two or more values that subscribe to a common schema. The objective is to generate fuzzy association rules using “properties” associated with these composite attributes. The exemplar application is the analysis of the nutrients contained in items found in grocery data sets. The paper commences with a review of the back ground and related work, and a formal definition of the CFARM concepts. The CFARM algorithm is then fully described and evaluated using both real and synthetic data sets.

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Rindengan, Altin J. "PERBANDINGAN ASOSSIATION RULE BERBENTUK BINER DAN FUZZY C-PARTITION PADA ANALISIS MARKET BASKET DALAM DATA MINING". JURNAL ILMIAH SAINS 12, n.º 2 (10 de noviembre de 2012): 135. http://dx.doi.org/10.35799/jis.12.2.2012.717.

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PERBANDINGAN ASOSSIATION RULE BERBENTUK BINER DAN FUZZY C-PARTITION PADA ANALISIS MARKET BASKET DALAM DATA MININGABSTRAKSalah satu analisis dalam data mining adalah market basket analysis untuk menganalisa kecenderungan pembelian suatu barang yang berasosiasi dengan barang yang lain. Dalam tulisan ini membahas aturan asosiasinya dengan mempertimbangkan jumlah item barang yang dibeli dalam satu transaksi. Asumsinya adalah keterkaitan pembelian suatu barang dengan barang yang lain dalam satu transaksi akan semakin kecil jika jumlah item barang yang dibeli semakin banyak. Tulisan ini menganalisa asosisasi antar item barang dengan membuat tabel transaksi dalam bentuk nilai fuzzy set dibandingkan dengan analisa asosiasi yang biasa dilakukan dalam bentuk biner. Berdasarkan analisis terhadap data yang digunakan memberikan hasil support dan confidence yang cenderung lebih kecil tetapi lebih realistis dibanding aturan asosisasi biasa. Keywords: analisis market basket, association rule, data mining, fuzzy c-partition.COMPARISON OF ASSOCIATION RULE WITH BINARY AND FUZZY C-PARTITION FORM AT MARKET BASKET ANALYSIS ON DATA MININGABSTRACTOne analysis in data mining is market basket analysis to analyze the purchase of a good trends associated with other items. In this paper discussing the association rules by considering the number of items purchased in one transaction. The assumption is that the purchase of a good relationship with the other items in one transaction will be smaller if the number of items purchased items more and more. This paper analyzes the association between the items of goods by making the transaction table in the form of fuzzy sets of values to compare with analysis of the usual associations in binary form. Based on the analysis of the data used to support and confidence of which tend to be smaller but more realistic than usual asosisasi rules. Keywords: market basket analysis, association rule, data mining, fuzzy c-partition.

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Petry, Frederick E. "Data Mining Approaches for Geo-Spatial Big Data". International Journal of Organizational and Collective Intelligence 3, n.º 1 (enero de 2012): 52–71. http://dx.doi.org/10.4018/joci.2012010104.

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The availability of a vast amount of heterogeneous information from a variety of sources ranging from satellite imagery to the Internet has been termed as the problem of Big Data. Currently there is a great emphasis on the huge amount of geophysical data that has a spatial basis or spatial aspects. To effectively utilize such volumes of data, data mining techniques are needed to manage discovery from such volumes of data. An important consideration for this sort of data mining is to extend techniques to manage the inherent uncertainty involved in such spatial data. In this paper the authors first provide overviews of uncertainty representations based on fuzzy, intuitionistic, and rough sets theory and data mining techniques. To illustrate the issues they focus on the application of the discovery of association rules in approaches for vague spatial data. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets are described. Finally an example of rule extraction for both fuzzy and rough set types of uncertainty representations is given

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Duan, Qing, Jian Li y Yu Wang. "The Application of Fuzzy Association Rule Mining in E-Commerce Information System Mining". Advanced Engineering Forum 6-7 (septiembre de 2012): 631–35. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.631.

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Data mining in e-commerce application is information into business knowledge in the process. First of all, the object of clear data mining to determine the theme of business applications; around the commercial main data collection source, and clean up the data conversion, integration processing technology, and selects the appropriate data mining algorithms to build data mining models. This paper presents the application of fuzzy association rule mining in E-commerce information system mining. Experimental data sets prove that the proposed algorithm is effective and reasonable.

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Qi, Wei Qiang, Yan Ran Li, Hai Feng Ye, Da Peng Duan y Xiu Chen Jiang. "Research on Classification of Partial Discharge of Switchgear Cabinets Based on a Novel Association Rule Algorithm". Applied Mechanics and Materials 448-453 (octubre de 2013): 3485–93. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3485.

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In order to assess switchgear insulation status, a novel association rule mining (ARM) algorithm is presented. It is used to recognize the severity of switchgear cabinet partial discharge. The algorithm uses fuzzy C-means clustering (FCM) to divide partial discharge feature interval, candidate sets meeting minimum support and minimum confidence are sought based on an improved Apriori algorithm. Multiple recursions and scans are performed on candidate sets to generate association rules library for classification. Fuzzy reasoning based on association rules are performed over multiple needle corona partial discharge signals sampled in 10KV switchgear cabinets. The results show that partial discharge classification rate using association rules is high and classification conclusions are accurate. It has provided theoretical basis and practical value for insulation status assessment of switchgear cabinets.

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Wang, Tianxiong y Meiyu Zhou. "Integrating rough set theory with customer satisfaction to construct a novel approach for mining product design rules". Journal of Intelligent & Fuzzy Systems 41, n.º 1 (11 de agosto de 2021): 331–53. http://dx.doi.org/10.3233/jifs-201829.

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When users choose a product, they consider the emotional experience triggered by the product form. In view of the fact that traditional kansei engineering can not effectively reflect the complex and changeable psychological factors of users, and it has not explored the complex relationship between customer satisfaction and perceptual demand characteristics. To address this problem, some uncertainty techniques including rough sets and fuzzy sets are applied to capture more accurate emotion knowledge. Therefore, this research proposes an integrated evaluation gird method (EGM), rough set theory (RST), continuous fuzzy kano model (CFKM), fuzzy weighted association rule mining method to extract the significant relationship between user needs and product morphological features. The EGM is applied to analyze the attractive factor of morphological characteristics of the product, and then the demand items with the highest satisfaction are analyzed through CFKM. The semantic difference method is combined to construct a decision table, and through attribute reduction and importance calculation to obtain the weight of the core product design items. In order to explore the non-linear relationship between design elements and kansei images, the fuzzy weighted association rule mining method was applied to obtain the set of frequent fuzzy weighted association rules based on evidence theory’s reliability indices of minimum support and confidence so as to realize user demand-driven product design. Taking the design of electric bicycle as an example, the experiment results show that the proposed method can help companies or designers develop products to generate good solutions for customer need.

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PAPADIMITRIOU, STERGIOS, SEFERINA MAVROUDI y SPIRIDON D. LIKOTHANASSIS. "MUTUAL INFORMATION CLUSTERING FOR EFFICIENT MINING OF FUZZY ASSOCIATION RULES WITH APPLICATION TO GENE EXPRESSION DATA ANALYSIS". International Journal on Artificial Intelligence Tools 15, n.º 02 (abril de 2006): 227–50. http://dx.doi.org/10.1142/s0218213006002643.

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Fuzzy association rules can reveal useful dependencies and interactions hidden in large gene expression data sets. However their derivation perplexes very difficult combinatorial problems that depend heavily on the size of these sets. The paper follows a divide and conquer approach to the problem that obtains computationally manageable solutions. Initially we cluster genes that more probably are associated. Thereafter, the fuzzy association rule extraction algorithms confront many but significantly reduced computationally problems that usually can be processed very fast. The clustering phase is accomplished by means of an approach based on mutual information (MI). This approach uses the mutual information as a similarity measure. However, the numerical evaluation of the MI is subtle. We experiment with the main methods and we compare between them. As the device that implements the mutual information clustering we use a SOM (Self-Organized Map) based approach that is capable of effectively incorporating supervised bias. After the mutual information clustering phase the fuzzy association rules are extracted locally on a per cluster basis. The paper presents an application of the techniques for mining the gene expression data. However, the presented techniques can easily be adapted and can be fruitful for intelligent exploration of any other similar data set as well.

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Cai, Wentian y Huijun Yao. "Research on Information Security Risk Assessment Method Based on Fuzzy Rule Set". Wireless Communications and Mobile Computing 2021 (22 de septiembre de 2021): 1–12. http://dx.doi.org/10.1155/2021/9663520.

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With the increasing complexity of the network structure and the increasing size of the network, various network security incidents pose an increasing threat to the security of computer systems and the network. Especially, in the network environment, the diversified intrusion methods and application environment make the security of the network more fragile. In order to improve information security, based on fuzzy rule sets, this paper proposes a fuzzy association rule mining algorithm based on fuzzy matrix and applies it to security event correlation. In addition, this paper combines the embedded system to construct an information security risk assessment system and sets the system performance based on the actual situation. Finally, this paper carries out experimental design to verify the performance of the system and analyzes the experimental results by mathematical statistics. From the experimental research, it can be seen that the system constructed in this paper has a certain effect.

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Supriyati, Endang, Mohammad Iqbal y Tutik Khotimah. "USING SIMILARITY DEGREES TO IMPROVE FUZZY MINING ASSOCIATION RULE BASED MODEL FOR ANALYZING IT ENTREPRENEURIAL TENDENCY". IIUM Engineering Journal 20, n.º 2 (2 de diciembre de 2019): 78–89. http://dx.doi.org/10.31436/iiumej.v20i2.1096.

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Higher education has great potential in producing new startups in the IT (Information Technology) field. Many choices influence students to become IT- entrepreneurs. Association Rule can be used to obtain a model by analysing data so that it can be used to make a rule to the IT entrepreneurship-student model, but the association algorithm has disadvantages in handling large datasets. We propose reducing candidate itemsets using degrees of fuzzy similarity. The membership function in fuzzy sets can be used to measure the quality of rules obtained. The purpose of this study is to improve the algorithm by evaluating the similarity of candidate itemsets to get a good quality rule. This research method has 2 phases, namely (1) calculating the membership function with similarity itemset and (2) applying fuzzy mining association rule. Phase 1 has several steps, including: preparation of a transaction database, the taxonomy process, and identification of similar itemset. Phase 2 has several steps as well. The first is defining membership functions, and the last is a fuzzy mining fuzzy association rule. In this study, a questionnaire was distributed to 1225 students who were members of the IT entrepreneurship program. The results of this study were reduced into 823 itemsets and produced an IT entrepreneurship rule model. ABSTRAK: Pendidikan tinggi mempunyai potensi besar dalam menghasilkan permulaan baru dalam bidang IT. Banyak pilihan mempengaruhi pelajar bagi menjadi usahawan-IT. Kaedah Bersekutu boleh digunakan bagi mendapatkan model dengan menganalisa data supaya ianya dapat digunakan menjadi model kepada pelajar keusahawanan-IT, namun algoritma bersekutu mempunyai kelemahan dalam mengendalikan dataset yang besar. Kami mencadangkan pengurangan bilangan set item menggunakan tahapan persamaan kabur. Fungsi ahli dalam set kabur dapat digunakan bagi mengukur kualiti aturan yang diperoleh. Tujuan kajian ini adalah bagi meningkatkan algoritma dengan menilai persamaan set item calon bagi mendapatkan aturan kualiti yang baik. Kaedah penyelidikan ini mempunyai 2 peringkat, iaitu (1) mengira fungsi ahli dengan set item persamaan dan (2) menerapkan aturan perlombongan bersekutu kabur. Peringkat 1 mempunyai beberapa langkah, iaitu: urus niaga pangkalan data, proses taksonomi, identifikasi set item yang sama. Tahap 2 mempunyai beberapa langkah, iaitu: menentukan fungsi keahlian, dan akhirnya, aturan perlombongan bersekutu. Dalam kajian ini, soal selidik telah diedarkan kepada 1225 pelajar yang menjadi ahli program keusahawanan IT. Dapatan kajian menunjukkan pengurangan nombor dataset kepada 823 set item dan menghasilkan model aturan teknologi keusahawanan IT.

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Tesis sobre el tema "Association rule mining. Fuzzy sets"

Cai, Chun Hing. "Mining association rules with weighted items". Hong Kong : Chinese University of Hong Kong, 1998. http://www.cse.cuhk.edu.hk/%7Ekdd/assoc%5Frule/thesis%5Fchcai.pdf.

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Thesis (M. Phil.)--Chinese University of Hong Kong, 1998.
Description based on contents viewed Mar. 13, 2007; title from title screen. Includes bibliographical references (p. 99-103). Also available in print.

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Miguel, Carlos Henrique 1983. "Método para identificação de perfis de produtos : estudo de caso automobilístico". [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/265791.

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Orientador: Antônio Batocchio
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica
Made available in DSpace on 2018-08-27T18:08:13Z (GMT). No. of bitstreams: 1 Miguel_CarlosHenrique_M.pdf: 3528187 bytes, checksum: 165344ab93862eb94649f13d1f4a8626 (MD5) Previous issue date: 2015
Resumo: O objetivo do trabalho foi elaborar um método de identificação de perfis de produto que representa os grupos de características frequentes do produto nas compras efetuadas por seus clientes. Foi feita uma revisão de literatura sobre quais áreas de gestão são influenciadas pela identificação de perfis de produtos, dentre elas: Planejamento de Demanda, Cadeia de Valor, Cadeia de Suprimentos e Cadeia Logística. Mais especificamente, as subáreas mais afetadas são Entrega de Fornecedores Chaves em base no Just In Time e Sistema de Reposição Contínua. As tecnologias de identificação eletrônica de produtos produzidos em série (e. g. RF ID, código de barras e código QR) são formas de identificar cada venda de produto a ser utilizado pelo método. Dentre as técnicas aplicadas no método, os Conjuntos Fuzzy foram utilizados para categorizar as características quantitativas dos produtos, que passaram a ser a entrada para a Análise de Carrinho de Compras, possibilitando determinar cada perfil de produto através de mineração de dados por regras de associação. O Apriori foi um algoritmo apropriado para realizar a Análise de Carrinho de Compras, pois realiza mineração por regras de associação de conjunto de itens frequentes utilizando as regras de interesse: suporte, confiança e lift. O algoritmo está presente no pacote Arules do programa estatístico R. O pacote ArulesViz, que está presente no programa estatístico R, permite visualizar de forma gráfica os relacionamentos entre os itens do produto. O método foi aplicado a uma base de dados de pesquisa do setor automobilístico, retornando com sucesso os perfis de automóvel frequentes dentre as compras efetuadas pelos clientes
Abstract: This study aimed to prepare a product profile identification method representing the groups of common characteristics of the product in the purchases made by its customers. A literature review was made on which areas of management are influenced by the identification of product profiles, such as: Demand Planning, Value Chain, Supply Chain and Logistic Chain. Specifically the Keys Suppliers Delivery sub-areas based on Just in Time and Continuous Replacement System are the most affected. The electronic identification technologies of products produced in series (e.g. RF ID, barcode and QR code) are ways to identify each product sale to be used by the method. Among the techniques applied in the method, Fuzzy Sets were used to categorize the quantitative characteristics of the products, which are now the entrance to the Market Basket Analysis, allowing to find each product profile through data mining for association rules. The Apriori was an appropriate algorithm to perform Market Basket Analysis, as done by mining association rule set of frequent item sets using the rules of interest: support, confidence and lift. The algorithm is present in Arules package of statistical software R. The ArulesViz package, which is present in the R statistical software, displays graphically the relationships between the items of the product. The method was applied to a research database of the automotive sector successfully returning the frequent car profiles from purchases made by customers
Mestrado
Materiais e Processos de Fabricação
Mestre em Engenharia Mecânica

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Shrestha, Anuj. "Association Rule Mining of Biological Field Data Sets". Thesis, North Dakota State University, 2017. https://hdl.handle.net/10365/28394.

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Association rule mining is an important data mining technique, yet, its use in association analysis of biological data sets has been limited. This mining technique was applied on two biological data sets, a genome and a damselfly data set. The raw data sets were pre-processed, and then association analysis was performed with various configurations. The pre-processing task involves minimizing the number of association attributes in genome data and creating the association attributes in damselfly data. The configurations include generation of single/maximal rules and handling single/multiple tier attributes. Both data sets have a binary class label and using association analysis, attributes of importance to each of these class labels are found. The results (rules) from association analysis are then visualized using graph networks by incorporating the association attributes like support and confidence, differential color schemes and features from the pre-processed data.
Bioinformatics Seed Grant Program NIH/UND
National Science Foundation (NSF) Grant IIA-1355466

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Pray, Keith A. "Apriori Sets And Sequences: Mining Association Rules from Time Sequence Attributes". Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0506104-150831/.

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Thesis (M.S.) -- Worcester Polytechnic Institute.
Keywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).

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Isik, Narin. "Fuzzy Spatial Data Cube Construction And Its Use In Association Rule Mining". Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606056/index.pdf.

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The popularity of spatial databases increases since the amount of the spatial data that need to be handled has increased by the use of digital maps, images from satellites, video cameras, medical equipment, sensor networks, etc. Spatial data are difficult to examine and extract interesting knowledge
hence, applications that assist decision-making about spatial data like weather forecasting, traffic supervision, mobile communication, etc. have been introduced. In this thesis, more natural and precise knowledge from spatial data is generated by construction of fuzzy spatial data cube and extraction of fuzzy association rules from it in order to improve decision-making about spatial data. This involves an extensive research about spatial knowledge discovery and how fuzzy logic can be used to develop it. It is stated that incorporating fuzzy logic to spatial data cube construction necessitates a new method for aggregation of fuzzy spatial data. We illustrate how this method also enhances the meaning of fuzzy spatial generalization rules and fuzzy association rules with a case-study about weather pattern searching. This study contributes to spatial knowledge discovery by generating more understandable and interesting knowledge from spatial data by extending spatial generalization with fuzzy memberships, extending the spatial aggregation in spatial data cube construction by utilizing weighted measures, and generating fuzzy association rules from the constructed fuzzy spatial data cube.

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Unal, Calargun Seda. "Fuzzy Association Rule Mining From Spatio-temporal Data: An Analysis Of Meteorological Data In Turkey". Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609308/index.pdf.

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Data mining is the extraction of interesting non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases. Association rule mining is a data mining method that seeks to discover associations among transactions encoded within a database. Data mining on spatio-temporal data takes into consideration the dynamics of spatially extended systems for which large amounts of spatial data exist, given that all real world spatial data exists in some temporal context. We need fuzzy sets in mining association rules from spatio-temporal databases since fuzzy sets handle the numerical data better by softening the sharp boundaries of data which models the uncertainty embedded in the meaning of data. In this thesis, fuzzy association rule mining is performed on spatio-temporal data using data cubes and Apriori algorithm. A methodology is developed for fuzzy spatio-temporal data cube construction. Besides the performance criteria interpretability, precision, utility, novelty, direct-to-the-point and visualization are defined to be the metrics for the comparison of association rule mining techniques. Fuzzy association rule mining using spatio-temporal data cubes and Apriori algorithm performed within the scope of this thesis are compared using these metrics. Real meteorological data (precipitation and temperature) for Turkey recorded between 1970 and 2007 are analyzed using data cube and Apriori algorithm in order to generate the fuzzy association rules.

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He, Yuanchen. "Fuzzy-Granular Based Data Mining for Effective Decision Support in Biomedical Applications". Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/12.

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Due to complexity of biomedical problems, adaptive and intelligent knowledge discovery and data mining systems are highly needed to help humans to understand the inherent mechanism of diseases. For biomedical classification problems, typically it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). In this dissertation, a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, is proposed to build such a DSS for binary classification problems in the biomedical domain. Empirical studies show that FARM-DS is competitive to state-of-the-art classifiers in terms of prediction accuracy. More importantly, FARs can provide strong decision support on disease diagnoses due to their easy interpretability. This dissertation also proposes a fuzzy-granular method to select informative and discriminative genes from huge microarray gene expression data. With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. Empirical studies show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies.

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Castro, Ricardo Ferreira Vieira de. "Análise de desempenho dos algoritmos Apriori e Fuzzy Apriori na extração de regras de associação aplicados a um Sistema de Detecção de Intrusos". Universidade do Estado do Rio de Janeiro, 2014. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=8137.

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A extração de regras de associação (ARM - Association Rule Mining) de dados quantitativos tem sido pesquisa de grande interesse na área de mineração de dados. Com o crescente aumento das bases de dados, há um grande investimento na área de pesquisa na criação de algoritmos para melhorar o desempenho relacionado a quantidade de regras, sua relevância e a performance computacional. O algoritmo APRIORI, tradicionalmente usado na extração de regras de associação, foi criado originalmente para trabalhar com atributos categóricos. Geralmente, para usá-lo com atributos contínuos, ou quantitativos, é necessário transformar os atributos contínuos, discretizando-os e, portanto, criando categorias a partir dos intervalos discretos. Os métodos mais tradicionais de discretização produzem intervalos com fronteiras sharp, que podem subestimar ou superestimar elementos próximos dos limites das partições, e portanto levar a uma representação imprecisa de semântica. Uma maneira de tratar este problema é criar partições soft, com limites suavizados. Neste trabalho é utilizada uma partição fuzzy das variáveis contínuas, que baseia-se na teoria dos conjuntos fuzzy e transforma os atributos quantitativos em partições de termos linguísticos. Os algoritmos de mineração de regras de associação fuzzy (FARM - Fuzzy Association Rule Mining) trabalham com este princípio e, neste trabalho, o algoritmo FUZZYAPRIORI, que pertence a esta categoria, é utilizado. As regras extraídas são expressas em termos linguísticos, o que é mais natural e interpretável pelo raciocício humano. Os algoritmos APRIORI tradicional e FUZZYAPRIORI são comparado, através de classificadores associativos, baseados em regras extraídas por estes algoritmos. Estes classificadores foram aplicados em uma base de dados relativa a registros de conexões TCP/IP que destina-se à criação de um Sistema de Detecção de Intrusos.
The mining of association rules of quantitative data has been of great research interest in the area of data mining. With the increasing size of databases, there is a large investment in research in creating algorithms to improve performance related to the amount of rules, its relevance and computational performance. The APRIORI algorithm, traditionally used in the extraction of association rules, was originally created to work with categorical attributes. In order to use continuous attributes, it is necessary to transform the continuous attributes, through discretization, into categorical attributes, where each categorie corresponds to a discrete interval. The more traditional discretization methods produce intervals with sharp boundaries, which may underestimate or overestimate elements near the boundaries of the partitions, therefore inducing an inaccurate semantical representation. One way to address this problem is to create soft partitions with smoothed boundaries. In this work, a fuzzy partition of continuous variables, which is based on fuzzy set theory is used. The algorithms for mining fuzzy association rules (FARM - Fuzzy Association Rule Mining) work with this principle, and, in this work, the FUZZYAPRIORI algorithm is used. In this dissertation, we compare the traditional APRIORI and the FUZZYAPRIORI, through classification results of associative classifiers based on rules extracted by these algorithms. These classifiers were applied to a database of records relating to TCP / IP connections that aims to create an Intrusion Detection System.

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Abraham, Lukáš. "Analýza dat síťové komunikace mobilních zařízení". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-432938.

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At the beginning, the work describes DNS and SSL/TLS protocols, it mainly deals with communication between devices using these protocols. Then we'll talk about data preprocessing and data cleaning. Furthermore, the thesis deals with basic data mining techniques such as data classification, association rules, information retrieval, regression analysis and cluster analysis. The next chapter we can read something about how to identify mobile devices on the network. We will evaluate data sets that contain collected data from communication between the above mentioned protocols, which will be used in the practical part. After that, we finally get to the design of a system for analyzing network communication data. We will describe the libraries, which we used and the entire system implementation. We will perform a large number of experiments, which we will finally evaluate.

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"Mining association rules with weighted items". 1998. http://library.cuhk.edu.hk/record=b5889513.

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Resumen

by Cai, Chun Hing.
Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.
Includes bibliographical references (leaves 109-114).
Abstract also in Chinese.
Acknowledgments --- p.ii
Abstract --- p.iii
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Main Categories in Data Mining --- p.1
Chapter 1.2 --- Motivation --- p.3
Chapter 1.3 --- Problem Definition --- p.4
Chapter 1.4 --- Experimental Setup --- p.5
Chapter 1.5 --- Outline of the thesis --- p.6
Chapter 2 --- Literature Survey on Data Mining --- p.8
Chapter 2.1 --- Statistical Approach --- p.8
Chapter 2.1.1 --- Statistical Modeling --- p.9
Chapter 2.1.2 --- Hypothesis testing --- p.10
Chapter 2.1.3 --- Robustness and Outliers --- p.11
Chapter 2.1.4 --- Sampling --- p.12
Chapter 2.1.5 --- Correlation --- p.15
Chapter 2.1.6 --- Quality Control --- p.16
Chapter 2.2 --- Artificial Intelligence Approach --- p.18
Chapter 2.2.1 --- Bayesian Network --- p.19
Chapter 2.2.2 --- Decision Tree Approach --- p.20
Chapter 2.2.3 --- Rough Set Approach --- p.21
Chapter 2.3 --- Database-oriented Approach --- p.23
Chapter 2.3.1 --- Characteristic and Classification Rules --- p.23
Chapter 2.3.2 --- Association Rules --- p.24
Chapter 3 --- Background --- p.27
Chapter 3.1 --- Iterative Procedure: Apriori Gen --- p.27
Chapter 3.1.1 --- Binary association rules --- p.27
Chapter 3.1.2 --- Apriori Gen --- p.29
Chapter 3.1.3 --- Closure Properties --- p.30
Chapter 3.2 --- Introduction of Weights --- p.31
Chapter 3.2.1 --- Motivation --- p.31
Chapter 3.3 --- Summary --- p.32
Chapter 4 --- Mining weighted binary association rules --- p.33
Chapter 4.1 --- Introduction of binary weighted association rules --- p.33
Chapter 4.2 --- Weighted Binary Association Rules --- p.34
Chapter 4.2.1 --- Introduction --- p.34
Chapter 4.2.2 --- Motivation behind weights and counts --- p.36
Chapter 4.2.3 --- K-support bounds --- p.37
Chapter 4.2.4 --- Algorithm for Mining Weighted Association Rules --- p.38
Chapter 4.3 --- Mining Normalized Weighted association rules --- p.43
Chapter 4.3.1 --- Another approach for normalized weighted case --- p.45
Chapter 4.3.2 --- Algorithm for Mining Normalized Weighted Association Rules --- p.46
Chapter 4.4 --- Performance Study --- p.49
Chapter 4.4.1 --- Performance Evaluation on the Synthetic Database --- p.49
Chapter 4.4.2 --- Performance Evaluation on the Real Database --- p.58
Chapter 4.5 --- Discussion --- p.65
Chapter 4.6 --- Summary --- p.66
Chapter 5 --- Mining Fuzzy Weighted Association Rules --- p.67
Chapter 5.1 --- Introduction to the Fuzzy Rules --- p.67
Chapter 5.2 --- Weighted Fuzzy Association Rules --- p.69
Chapter 5.2.1 --- Problem Definition --- p.69
Chapter 5.2.2 --- Introduction of Weights --- p.71
Chapter 5.2.3 --- K-bound --- p.73
Chapter 5.2.4 --- Algorithm for Mining Fuzzy Association Rules for Weighted Items --- p.74
Chapter 5.3 --- Performance Evaluation --- p.77
Chapter 5.3.1 --- Performance of the algorithm --- p.77
Chapter 5.3.2 --- Comparison of unweighted and weighted case --- p.79
Chapter 5.4 --- Note on the implementation details --- p.81
Chapter 5.5 --- Summary --- p.81
Chapter 6 --- Mining weighted association rules with sampling --- p.83
Chapter 6.1 --- Introduction --- p.83
Chapter 6.2 --- Sampling Procedures --- p.84
Chapter 6.2.1 --- Sampling technique --- p.84
Chapter 6.2.2 --- Algorithm for Mining Weighted Association Rules with Sampling --- p.86
Chapter 6.3 --- Performance Study --- p.88
Chapter 6.4 --- Discussion --- p.91
Chapter 6.5 --- Summary --- p.91
Chapter 7 --- Database Maintenance with Quality Control method --- p.92
Chapter 7.1 --- Introduction --- p.92
Chapter 7.1.1 --- Motivation of using the quality control method --- p.93
Chapter 7.2 --- Quality Control Method --- p.94
Chapter 7.2.1 --- Motivation of using Mil. Std. 105D --- p.95
Chapter 7.2.2 --- Military Standard 105D Procedure [12] --- p.95
Chapter 7.3 --- Mapping the Database Maintenance to the Quality Control --- p.96
Chapter 7.3.1 --- Algorithm for Database Maintenance --- p.98
Chapter 7.4 --- Performance Evaluation --- p.102
Chapter 7.5 --- Discussion --- p.104
Chapter 7.6 --- Summary --- p.105
Chapter 8 --- Conclusion and Future Work --- p.106
Chapter 8.1 --- Summary of the Thesis --- p.106
Chapter 8.2 --- Conclusions --- p.107
Chapter 8.3 --- Future Work --- p.108
Bibliography --- p.108
Appendix --- p.115
Chapter A --- Generating a random number --- p.115
Chapter B --- Hypergeometric distribution --- p.116
Chapter C --- Quality control tables --- p.117
Chapter D --- Rules extracted from the database --- p.120

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