One of the most popular data mining methods is frequent itemset and association rule discovery. Mined patterns are usually stored in a relational database for future use. Analyzing discovered patterns requires excessi...
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ISBN:
(纸本)9729881618
One of the most popular data mining methods is frequent itemset and association rule discovery. Mined patterns are usually stored in a relational database for future use. Analyzing discovered patterns requires excessive subset search querying in large amount of database tuples. Indexes available in relational database systems are not well suited for this class of queries. In this paper we study the performance of four different indexing techniques that aim at speeding up data mining queries, particularly improving set inclusion queries in relational databases. We investigate the performance of those indexes under varying factors including the size of the database, the size of the query, the selectivity of the query, etc. Our experiments show significant improvements over traditional database access methods using standard B+ tree indexes.
We describe the organization of a Server-side platform supporting the execution of Event-Driven Mashups (i.e., composite applications combining services and smart objects through events). To support a large number of ...
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this book includes papers presented at SOCO 2018, CISIS 2018 and ICEUTE 2018, all held in the beautiful and historic city of San Sebastian (Spain), in June 2018. Soft computing represents a collection or set of comput...
ISBN:
(纸本)9783319941196
this book includes papers presented at SOCO 2018, CISIS 2018 and ICEUTE 2018, all held in the beautiful and historic city of San Sebastian (Spain), in June 2018. Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate, and analyze highly complex issues and phenomena. After a rigorous peer-review process, the 13th SOCO 2018 international Program Committee selected 41 papers, with a special emphasis on optimization, modeling and control using soft computing techniques and soft computing applications in the field of industrial and environmental enterprises. the aim of the 11th CISIS 2018 conference was to offer a meeting opportunity for academic and industry researchers from the vast areas of computational intelligence, information security, and data mining. the need for intelligent, flexible behaviour by large, complex systems, especially in mission-critical domains, was the catalyst for the overall event. Eight of the papers included in the book were selected by the CISIS 2018 international Program Committee. the international Program Committee of ICEUTE 2018 selected 11 papers for inclusion in these conferenceproceedings.
Mobility is one of the most important issues in the next generation of Wireless Sensor Networks. In the future internet oriented to internet-ofthings and awareness context solutions is essential to support mobility an...
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this paper uses agent-based modeling (ABM) to identify patterns in heroin addiction shaped by the market in which the drug is sold. Research has characterized the biological motivations and social behaviors of people ...
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ISBN:
(纸本)1844690172
this paper uses agent-based modeling (ABM) to identify patterns in heroin addiction shaped by the market in which the drug is sold. Research has characterized the biological motivations and social behaviors of people addicted to heroin. Animal research outlines mechanisms associated with an addiction to opiates. Behavioral studies described how heroin users experience addiction, as well as its various outcomes. Finally, scientists have researched how heroin is acquired and distributed through various local drug markets. Using ABM, this paper unifies these disconnected domains simulating how heroin addiction patterns are generated through heroin markets. Extracting data from an ethnographic study of how a local heroin market operated, a market model was developed. Several different types of agents sell the drug in this model. Customer agents use heroin and when they run out of drug they must purchase more through the market. Data evaluate customer agent "addiction levels" during the 12-month simulation of the market. Preliminary analysis reveals "binge/crash," "stepped" and "stable" patterns in customer addiction levels.
Using only the existence and uniqueness of trajectories for a generic dynamic system with inputs, we define and examine eight types of forward and backward reachability constructs. If the input is treated in a worst-c...
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ISBN:
(纸本)9783540714927
Using only the existence and uniqueness of trajectories for a generic dynamic system with inputs, we define and examine eight types of forward and backward reachability constructs. If the input is treated in a worst-case fashion, any forward or backward reach set or tube can be used for safety analysis, but if the input is treated in a best-case fashion only the backward reach tube always provides the correct results. Fortunately, forward and backward algorithms can be exchanged if wellposed reverse time trajectories can be defined. Unfortunately, backward reachability constructs are more likely to suffer from numerical stability issues, especially in systems with significant contraction-the very systems where forward simulation and reachability are most effective.
Particle tracking is an essential part of any high-energy physics experiment. Well-known tracking algorithms based on the Kalman filter are not scaling well withthe amounts of data being produced in modern experiment...
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In many domains, the data objects are described in terms of a large number of features. the pipelined data mining approach introduced in [1] using two clustering algorithms in combination with rough sets and extended ...
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ISBN:
(纸本)3540362975
In many domains, the data objects are described in terms of a large number of features. the pipelined data mining approach introduced in [1] using two clustering algorithms in combination with rough sets and extended with genetic programming, is investigated withthe purpose of discovering important subsets of attributes in high dimensional data. their classification ability is described in terms of both collections of rules and analytic functions obtained by genetic programming (gene expression programming). the Leader and several k-means algorithms are used as procedures for attribute set simplification of the information systems later presented to rough sets algorithms. Visual data raining techniques including virtual reality were used for inspecting results. the data mining process is setup using high throughput distributed computing techniques. this approach was applied to Breast Cancer microarray data and it led to subsets of genes with high discrimination power with respect to the decision classes.
We propose the use of a particle filter as a solution to the rigid shape-based registration problem commonly found in computer-assisted surgery. this approach is especially useful where there are only a few registrati...
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A sparse kernel density estimator is derived based on the zero-norm constraint, in which the zero-norm of the kernel weights is incorporated to enhance model sparsity. the classical Parzen window estimate is adopted a...
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