Página 1 dos resultados de 106 itens digitais encontrados em 0.020 segundos

Sequential pattern mining of price interactions

Marques, Nuno C.; Cavique, Luís
Fonte: EPIA Publicador: EPIA
Tipo: Conferência ou Objeto de Conferência
Publicado em //2013 Português
Relevância na Pesquisa
57.6016%
Conferência realizada em Angra do Heroísmo, Açores, de 9-12 de Setembro de 2013; The computational analysis of large quantities of data is an important asset for the economic study of interactions among social agents. However, most of available frequent pattern discovery techniques result in a huge number of rules and scalability problems that end up requiring unnecessary subjectivity in data interpretation. This work presents Ramex-Forum, a visualization technique that can highlight important relations often hidden in economic data. A case study using recent asset prices on global economic data confi rm the usefulness of the approach for expressing economic influence cues as poly-trees.

Enhancing spatial association rule mining in geographic databases; Melhorando a Mineração de Regras de Associação Espacial em Bancos de Dados Geográficos

Bogorny, Vania
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Tese de Doutorado Formato: application/pdf
Português
Relevância na Pesquisa
69.281973%
A técnica de mineração de regras de associação surgiu com o objetivo de encontrar conhecimento novo, útil e previamente desconhecido em bancos de dados transacionais, e uma grande quantidade de algoritmos de mineração de regras de associação tem sido proposta na última década. O maior e mais bem conhecido problema destes algoritmos é a geração de grandes quantidades de conjuntos freqüentes e regras de associação. Em bancos de dados geográficos o problema de mineração de regras de associação espacial aumenta significativamente. Além da grande quantidade de regras e padrões gerados a maioria são associações do domínio geográfico, e são bem conhecidas, normalmente explicitamente representadas no esquema do banco de dados. A maioria dos algoritmos de mineração de regras de associação não garantem a eliminação de dependências geográficas conhecidas a priori. O resultado é que as mesmas associações representadas nos esquemas do banco de dados são extraídas pelos algoritmos de mineração de regras de associação e apresentadas ao usuário. O problema de mineração de regras de associação espacial pode ser dividido em três etapas principais: extração dos relacionamentos espaciais, geração dos conjuntos freqüentes e geração das regras de associação. A primeira etapa é a mais custosa tanto em tempo de processamento quanto pelo esforço requerido do usuário. A segunda e terceira etapas têm sido consideradas o maior problema na mineração de regras de associação em bancos de dados transacionais e tem sido abordadas como dois problemas diferentes: “frequent pattern mining” e “association rule mining”. Dependências geográficas bem conhecidas aparecem nas três etapas do processo. Tendo como objetivo a eliminação dessas dependências na mineração de regras de associação espacial essa tese apresenta um framework com três novos métodos para mineração de regras de associação utilizando restrições semânticas como conhecimento a priori. O primeiro método reduz os dados de entrada do algoritmo...

Multi-relational algorithm for mining association rules in large databases

Valêncio, Carlos Roberto; Oyama, Fernando Takeshi; Ichiba, Fernando Tochio; De Souza, Rogéria Cristiane Gratão
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Conferência ou Objeto de Conferência Formato: 269-274
Português
Relevância na Pesquisa
58.382275%
Multi-relational data mining enables pattern mining from multiple tables. The existing multi-relational mining association rules algorithms are not able to process large volumes of data, because the amount of memory required exceeds the amount available. The proposed algorithm MRRadix presents a framework that promotes the optimization of memory usage. It also uses the concept of partitioning to handle large volumes of data. The original contribution of this proposal is enable a superior performance when compared to other related algorithms and moreover successfully concludes the task of mining association rules in large databases, bypass the problem of available memory. One of the tests showed that the MR-Radix presents fourteen times less memory usage than the GFP-growth. © 2011 IEEE.

SeqNLS: Nuclear Localization Signal Prediction Based on Frequent Pattern Mining and Linear Motif Scoring

Lin, Jhih-rong; Hu, Jianjun
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 29/10/2013 Português
Relevância na Pesquisa
58.10879%
Nuclear localization signals (NLSs) are stretches of residues in proteins mediating their importing into the nucleus. NLSs are known to have diverse patterns, of which only a limited number are covered by currently known NLS motifs. Here we propose a sequential pattern mining algorithm SeqNLS to effectively identify potential NLS patterns without being constrained by the limitation of current knowledge of NLSs. The extracted frequent sequential patterns are used to predict NLS candidates which are then filtered by a linear motif-scoring scheme based on predicted sequence disorder and by the relatively local conservation (IRLC) based masking.

A primer to frequent itemset mining for bioinformatics

Naulaerts, Stefan; Meysman, Pieter; Bittremieux, Wout; Vu, Trung Nghia; Vanden Berghe, Wim; Goethals, Bart; Laukens, Kris
Fonte: Oxford University Press Publicador: Oxford University Press
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
48.868154%
Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences.

VISTREE: uma linguagem visual para análise de padrões arborescentes e para especificação de restrições em um ambiente de mineração de árvores

Felício, Crícia Zilda
Fonte: Universidade Federal de Uberlândia Publicador: Universidade Federal de Uberlândia
Tipo: Dissertação
Português
Relevância na Pesquisa
49.109243%
A mineração de padrões freqüentes em dados representados por estruturas mais complexas como árvores e grafos vêm crescendo muito nos últimos tempos. Entre as razões para esse crescimento está o fato do padrão arborescente ou em forma de grafo possuir mais informações do que os padrões seqüenciais, e na possibilidade de aplicação desse tipo de mineração em várias áreas como XML Mining, Web Mining e Bioinformática. Um problema que ocorre na mineração de padrões em geral é a grande quantidade de padrões gerados; sendo que muitos deles nem são do interesse do usuário. A diminuição da quantidade de padrões gerados pode ser feita restringido o tipo de padrão produzido através de especificações do usuário. Mesmo incorporando restrições no processo de mineração, a quantidade de padrões arborescentes minerados é grande, o que torna necessário uma ferramenta de análise dos padrões, possibilitando ao usuário especificar consultas para extrair da massa de padrões minerados aqueles que satisfazem os critérios de seleção da consulta. A mineração de padrões com restrição, visa obter como resultado de um processo de mineração apenas os padrões de real interesse do usuário. Uma restrição sobre padrões será representada de acordo com a estrutura dos mesmos. Para a mineração de padrões seqüencias uma forma de representá-la seria através de expressões regulares...

CobMiner - Mineração de padrões arborescentes com restrições

Silva, Nyara de Araújo
Fonte: Universidade Federal de Uberlândia Publicador: Universidade Federal de Uberlândia
Tipo: Dissertação
Português
Relevância na Pesquisa
49.31207%
Há muito trabalho em mineração de padrões com foco em estruturas de dados simples como itemsets ou seqüência de itemsets. Entretanto, recentes aplicações utilizam dados mais complexos como componentes químicos, estruturas proteicas, rede social, XML e logs da Web, exigindo estruturas de dados mais sofisticadas (árvores ou grafos) para serem especificadas. Aqui, padrões de interesse não envolvem apenas valores de objetos frequentes labels que aparecem em árvores (ou grafos), mas também topologias específicas frequentes encontradas nessas estruturas. A mineração de padrões de árvores frequentes tem sido bastante estudada, com a motivação do crescente interesse e aplicabilidade em diferentes áreas (Web Mining, Bioinformática, etc.). Porém, os sistemas convencionais de mineração de árvores permitiam ao usuário apenas definir o suporte mínimo como mecanismo de filtro dos padrões a serem minerados. Após o processo de mineração, um árduo trabalho é necessário para filtrar os padrões de interesse dos usuários. Nessa dissertação, propomos o algoritmo CobMiner, Constrained-based Miner, um algoritmo de mineração de padrões arborescentes, incorporando ao processo de mineração os Autômatos de Árvores...

Basic Framework of CATSIM Tree for Efficient Frequent Pattern Mining

Patel, Sanjay; Sankalchand Patel College of Engineering; Garg, Sanjay; A.D. Patel Institute of Technology
Fonte: Editora da UFLA Publicador: Editora da UFLA
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; Peer-reviewed Article Formato: application/pdf
Publicado em 26/08/2015 Português
Relevância na Pesquisa
68.288076%

Using Apriori with WEKA for Frequent Pattern Mining

Tanna, Paresh; Ghodasara, Yogesh
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 28/06/2014 Português
Relevância na Pesquisa
68.765757%
Knowledge exploration from the large set of data,generated as a result of the various data processing activities due to data mining only. Frequent Pattern Mining is a very important undertaking in data mining. Apriori approach applied to generate frequent item set generally espouse candidate generation and pruning techniques for the satisfaction of the desired objective. This paper shows how the different approaches achieve the objective of frequent mining along with the complexities required to perform the job. This paper demonstrates the use of WEKA tool for association rule mining using Apriori algorithm.; Comment: 5 Pages, 4 Figures, "Published with International Journal of Engineering Trends and Technology (IJETT)"

Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation

Wang, Pichao; Li, Wanqing; Ogunbona, Philip; Gao, Zhimin; Zhang, Hanling
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 14/09/2014 Português
Relevância na Pesquisa
58.256514%
Recently, mid-level features have shown promising performance in computer vision. Mid-level features learned by incorporating class-level information are potentially more discriminative than traditional low-level local features. In this paper, an effective method is proposed to extract mid-level features from Kinect skeletons for 3D human action recognition. Firstly, the orientations of limbs connected by two skeleton joints are computed and each orientation is encoded into one of the 27 states indicating the spatial relationship of the joints. Secondly, limbs are combined into parts and the limb's states are mapped into part states. Finally, frequent pattern mining is employed to mine the most frequent and relevant (discriminative, representative and non-redundant) states of parts in continuous several frames. These parts are referred to as Frequent Local Parts or FLPs. The FLPs allow us to build powerful bag-of-FLP-based action representation. This new representation yields state-of-the-art results on MSR DailyActivity3D and MSR ActionPairs3D.

Extending Task Parallelism for Frequent Pattern Mining

Kambadur, Prabhanjan; Ghoting, Amol; Gupta, Anshul; Lumsdaine, Andrew
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 07/11/2012 Português
Relevância na Pesquisa
68.5529%
Algorithms for frequent pattern mining, a popular informatics application, have unique requirements that are not met by any of the existing parallel tools. In particular, such applications operate on extremely large data sets and have irregular memory access patterns. For efficient parallelization of such applications, it is necessary to support dynamic load balancing along with scheduling mechanisms that allow users to exploit data locality. Given these requirements, task parallelism is the most promising of the available parallel programming models. However, existing solutions for task parallelism schedule tasks implicitly and hence, custom scheduling policies that can exploit data locality cannot be easily employed. In this paper we demonstrate and characterize the speedup obtained in a frequent pattern mining application using a custom clustered scheduling policy in place of the popular Cilk-style policy. We present PFunc, a novel task parallel library whose customizable task scheduling and task priorities facilitated the implementation of our clustered scheduling policy.

Efficient Analysis of Pattern and Association Rule Mining Approaches

Slimani, Thabet; Lazzez, Amor
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/02/2014 Português
Relevância na Pesquisa
49.295547%
The process of data mining produces various patterns from a given data source. The most recognized data mining tasks are the process of discovering frequent itemsets, frequent sequential patterns, frequent sequential rules and frequent association rules. Numerous efficient algorithms have been proposed to do the above processes. Frequent pattern mining has been a focused topic in data mining research with a good number of references in literature and for that reason an important progress has been made, varying from performant algorithms for frequent itemset mining in transaction databases to complex algorithms, such as sequential pattern mining, structured pattern mining, correlation mining. Association Rule mining (ARM) is one of the utmost current data mining techniques designed to group objects together from large databases aiming to extract the interesting correlation and relation among huge amount of data. In this article, we provide a brief review and analysis of the current status of frequent pattern mining and discuss some promising research directions. Additionally, this paper includes a comparative study between the performance of the described approaches.; Comment: 14 pages, 3 figures. arXiv admin note: text overlap with arXiv:1312.4800; and with arXiv:1109.2427 by other authors

Relationship-aware sequential pattern mining

Stendardo, Nabil; Kalousis, Alexandros
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 21/12/2012 Português
Relevância na Pesquisa
48.962524%
Relationship-aware sequential pattern mining is the problem of mining frequent patterns in sequences in which the events of a sequence are mutually related by one or more concepts from some respective hierarchical taxonomies, based on the type of the events. Additionally events themselves are also described with a certain number of taxonomical concepts. We present RaSP an algorithm that is able to mine relationship-aware patterns over such sequences; RaSP follows a two stage approach. In the first stage it mines for frequent type patterns and {\em all} their occurrences within the different sequences. In the second stage it performs hierarchical mining where for each frequent type pattern and its occurrences it mines for more specific frequent patterns in the lower levels of the taxonomies. We test RaSP on a real world medical application, that provided the inspiration for its development, in which we mine for frequent patterns of medical behavior in the antibiotic treatment of microbes and show that it has a very good computational performance given the complexity of the relationship-aware sequential pattern mining problem.

Foundation for Frequent Pattern Mining Algorithms Implementation

Tanna, Prof. Paresh; Ghodasara, Dr. Yogesh
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 07/02/2014 Português
Relevância na Pesquisa
69.06167%
As with the development of the IT technologies, the amount of accumulated data is also increasing. Thus the role of data mining comes into picture. Association rule mining becomes one of the significant responsibilities of descriptive technique which can be defined as discovering meaningful patterns from large collection of data. The frequent pattern mining algorithms determine the frequent patterns from a database. Mining frequent itemset is very fundamental part of association rule mining. Many algorithms have been proposed from last many decades including majors are Apriori, Direct Hashing and Pruning, FP-Growth, ECLAT etc. The aim of this study is to analyze the existing techniques for mining frequent patterns and evaluate the performance of them by comparing Apriori and DHP algorithms in terms of candidate generation, database and transaction pruning. This creates a foundation to develop newer algorithm for frequent pattern mining.; Comment: 5 pages, Published with International Journal of Computer Trends and Technology (IJCTT)

Activity Modeling in Smart Home using High Utility Pattern Mining over Data Streams

J, Menaka Gandhi.; Gayathri, K. S.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 25/06/2013 Português
Relevância na Pesquisa
58.98502%
Smart home technology is a better choice for the people to care about security, comfort and power saving as well. It is required to develop technologies that recognize the Activities of Daily Living (ADLs) of the residents at home and detect the abnormal behavior in the individual's patterns. Data mining techniques such as Frequent pattern mining (FPM), High Utility Pattern (HUP) Mining were used to find those activity patterns from the collected sensor data. But applying the above technique for Activity Recognition from the temporal sensor data stream is highly complex and challenging task. So, a new approach is proposed for activity recognition from sensor data stream which is achieved by constructing Frequent Pattern Stream tree (FPS - tree). FPS is a sliding window based approach to discover the recent activity patterns over time from data streams. The proposed work aims at identifying the frequent pattern of the user from the sensor data streams which are later modeled for activity recognition. The proposed FPM algorithm uses a data structure called Linked Sensor Data Stream (LSDS) for storing the sensor data stream information which increases the efficiency of frequent pattern mining algorithm through both space and time. The experimental results show the efficiency of the proposed algorithm and this FPM is further extended for applying for power efficiency using HUP to detect the high usage of power consumption of residents at smart home.; Comment: This research paper consists of 7 pages...

Efficient Support Coupled Frequent Pattern Mining Over Progressive Databases

Keshavamurthy, B. N.; Sharma, Mitesh; Toshniwal, Durga
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 29/05/2010 Português
Relevância na Pesquisa
58.81004%
There have been many recent studies on sequential pattern mining. The sequential pattern mining on progressive databases is relatively very new, in which we progressively discover the sequential patterns in period of interest. Period of interest is a sliding window continuously advancing as the time goes by. As the focus of sliding window changes, the new items are added to the dataset of interest and obsolete items are removed from it and become up to date. In general, the existing proposals do not fully explore the real world scenario, such as items associated with support in data stream applications such as market basket analysis. Thus mining important knowledge from supported frequent items becomes a non trivial research issue. Our proposed novel approach efficiently mines frequent sequential pattern coupled with support using progressive mining tree.; Comment: 10 Pages, IJDMS

Efficient Candidacy Reduction For Frequent Pattern Mining

Shahraki, Mohammad Nadimi; Mustapha, Norwati; Sulaiman, Md Nasir B; Mamat, Ali B
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 13/01/2010 Português
Relevância na Pesquisa
68.934937%
Certainly, nowadays knowledge discovery or extracting knowledge from large amount of data is a desirable task in competitive businesses. Data mining is a main step in knowledge discovery process. Meanwhile frequent patterns play central role in data mining tasks such as clustering, classification, and association analysis. Identifying all frequent patterns is the most time consuming process due to a massive number of candidate patterns. For the past decade there have been an increasing number of efficient algorithms to mine the frequent patterns. However reducing the number of candidate patterns and comparisons for support counting are still two problems in this field which have made the frequent pattern mining one of the active research themes in data mining. A reasonable solution is identifying a small candidate pattern set from which can generate all frequent patterns. In this paper, a method is proposed based on a new candidate set called candidate head set or H which forms a small set of candidate patterns. The experimental results verify the accuracy of the proposed method and reduction of the number of candidate patterns and comparisons.; Comment: 8 pages IEEE format, International Journal of Computer Science and Information Security...

Abstract Representations and Frequent Pattern Discovery

Ozkural, Eray
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 09/02/2012 Português
Relevância na Pesquisa
58.46452%
We discuss the frequent pattern mining problem in a general setting. From an analysis of abstract representations, summarization and frequent pattern mining, we arrive at a generalization of the problem. Then, we show how the problem can be cast into the powerful language of algorithmic information theory. This allows us to formulate a simple algorithm to mine for all frequent patterns.

Mining Rooted Ordered Trees under Subtree Homeomorphism

Chehreghani, Mostafa Haghir; Bruynooghe, Maurice
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
49.038994%
Mining frequent tree patterns has many applications in different areas such as XML data, bioinformatics and World Wide Web. The crucial step in frequent pattern mining is frequency counting, which involves a matching operator to find occurrences (instances) of a tree pattern in a given collection of trees. A widely used matching operator for tree-structured data is subtree homeomorphism, where an edge in the tree pattern is mapped onto an ancestor-descendant relationship in the given tree. Tree patterns that are frequent under subtree homeomorphism are usually called embedded patterns. In this paper, we present an efficient algorithm for subtree homeomorphism with application to frequent pattern mining. We propose a compact data-structure, called occ, which stores only information about the rightmost paths of occurrences and hence can encode and represent several occurrences of a tree pattern. We then define efficient join operations on the occ data-structure, which help us count occurrences of tree patterns according to occurrences of their proper subtrees. Based on the proposed subtree homeomorphism method, we develop an effective pattern mining algorithm, called TPMiner. We evaluate the efficiency of TPMiner on several real-world and synthetic datasets. Our extensive experiments confirm that TPMiner always outperforms well-known existing algorithms...

Structural advances for pattern discovery in multi-relational databases

Kanodia, Juveria
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado Formato: 15299 bytes; 1045261 bytes; 148995 bytes; 1 bytes; 2061 bytes; 698 bytes; 5468 bytes; 49 bytes; 15299 bytes; 1045261 bytes; application/pdf; application/pdf; text/plain; text/plain; text/plain; application/octet-stream; application/octet-stream; applicati
Português
Relevância na Pesquisa
79.155864%
With ever-growing storage needs and drift towards very large relational storage settings, multi-relational data mining has become a prominent and pertinent field for discovering unique and interesting relational patterns. As a consequence, a whole suite of multi-relational data mining techniques is being developed. These techniques may either be extensions to the already existing single-table mining techniques or may be developed from scratch. For the traditionalists, single-table mining algorithms can be used to work on multi-relational settings by making inelegant and time consuming joins of all target relations. However, complex relational patterns cannot be expressed in a single-table format and thus, cannot be discovered. This work presents a new multi-relational frequent pattern mining algorithm termed Multi-Relational Frequent Pattern Growth (MRFP Growth). MRFP Growth is capable of mining multiple relations, linked with referential integrity, for frequent patterns that satisfy a user specified support threshold. Empirical results on MRFP Growth performance and its comparison with the state-of-the-art multirelational data mining algorithms like WARMR and Decentralized Apriori are discussed at length. MRFP Growth scores over the latter two techniques in number of patterns generated and speed. The realm of multi-relational clustering is also explored in this thesis. A multi-Relational Item Clustering approach based on Hypergraphs (RICH) is proposed. Experimentally RICH combined with MRFP Growth proves to be a competitive approach for clustering multi-relational data. The performance and iii quality of clusters generated by RICH are compared with other clustering algorithms. Finally...