Son derece basit bir şekilde ukash bozdurma ve alma işlemlerini yapabilir, istediğiniz zaman sahip bulunduğunuz ukash sepetinde bulunan tutarınızı vakit kaybetmeden yine nakit paraya dönüştürebilmeniz mümkündür. Ukash neye yarar sorusuna en iyi cevabı güvenirliliğinin yanısıra Ukash destek noktası ile sürekli cüzdanınız emniyette diyerek vermekte olan bu sistemde Ukash kredi kartı gibi olup tüm kullanımlarda sizlerde yeni bir pin kodu elde ederek paranızın güvenliğini en üst noktaya ulaştırmayı başarabilen bir sistemdir cevabı verilebilir.
Published Journals
Ukash kart ve ukash
Posted by Themarcer 103 days ago (Editorial)
Parallel Association Rule Mining with Minimum Inter-Processor Communication
Posted by microgen 503 days ago (Editorial)
Existing parallel association rule mining algorithms suffer from many problems when mining massive transactional datasets. One major problem is that most of the parallel algorithms for a shared nothing environment are Aprioribased algorithms. Apriori-based algorithms are proven to be not scalable due to many reasons, mainly: (1) the repetitive I/O disk scans, (2) the huge computation and communication involved during the candidacy generation. This paper proposes a new disk-based parallel association rule mining algorithm called Inverted Matrix, which
achieves its efficiency by applying three new ideas. First, transactional data is converted into a new database layout called Inverted Matrix that prevents multiple scanning of the database during the mining phase, in which finding globally frequent patterns could be achieved in less than a full scan with random access. This data structure is replicated among the parallel nodes. Second, for each frequent item assigned to a parallel node, a relatively small independent tree is built summarizing co-occurrences. Finally, a simple and non-recursive mining process reduces the memory requirements as minimum candidacy generation and counting is needed, and no communication between nodes
is required to generate all globally frequent patterns.
achieves its efficiency by applying three new ideas. First, transactional data is converted into a new database layout called Inverted Matrix that prevents multiple scanning of the database during the mining phase, in which finding globally frequent patterns could be achieved in less than a full scan with random access. This data structure is replicated among the parallel nodes. Second, for each frequent item assigned to a parallel node, a relatively small independent tree is built summarizing co-occurrences. Finally, a simple and non-recursive mining process reduces the memory requirements as minimum candidacy generation and counting is needed, and no communication between nodes
is required to generate all globally frequent patterns.
Research for Parallel Apriori Algorithm based on MPI
Posted by microgen 503 days ago (Editorial)
In order to improve the efficiency of Apriori mining algorithm for the Ultra-large-scale data sets, based on the partition for the candidate itemsets, this paper presents a parallel algorithm for mining association rules which directly using MPI for passing message base on the master-slave structural model. Simulation analysis showed that the mining time of the algorithm which proposed in this paper has a higher degree of shortening compared with the algorithm of Apriori. There have good parallelism and scalability especially for large-scale database mining.
Programming Parallel Apriori Algorithms for Mining Association Rules
Posted by microgen 506 days ago (Editorial)
Many parallel programming languages provide a set of low-level parallel constructs which lead to programs that are difficult to design, implement, debug, and maintain. In this paper, we present a parallel programming paradigm using a sequential programming language that encapsulates low-level details of distributed and concurrent programming techniques and makes the details transparent to programmers. This paradigm allows programmers to program on several different platforms in a sequential language that they are most familiar with. In addition, the paradigm provides high-level language constructs for programmers to implement various distributed and parallel algorithms. The programming paradigm is illustrated with two parallel Apriori algorithms in data mining.
Parallel Association Rule Mining with Minimum Inter-Processor Communication
Posted by microgen 506 days ago (Editorial)
Existing parallel association rule mining algorithms suffer from many problems when mining massive transactional datasets. One major problem is that most of the parallel algorithms for a shared nothing environment are Aprioribased algorithms. Apriori-based algorithms are proven to be not scalable due to many reasons, mainly: (1) the repetitive I/O disk scans, (2) the huge computation and communication involved during the candidacy generation. This paper proposes a new disk-based parallel association rule mining algorithm called Inverted Matrix, which
achieves its efficiency by applying three new ideas. First, transactional data is converted into a new database layout called Inverted Matrix that prevents multiple scanning of the database during the mining phase, in which finding globally frequent patterns could be achieved in less than a full scan with random access. This data structure is replicated among the parallel nodes. Second, for each frequent item assigned to a parallel node, a relatively small independent tree is built summarizing co-occurrences. Finally, a simple and non-recursive mining process reduces the memory requirements as minimum candidacy generation and counting is needed, and no communication between nodes is required to generate all globally frequent patterns.
achieves its efficiency by applying three new ideas. First, transactional data is converted into a new database layout called Inverted Matrix that prevents multiple scanning of the database during the mining phase, in which finding globally frequent patterns could be achieved in less than a full scan with random access. This data structure is replicated among the parallel nodes. Second, for each frequent item assigned to a parallel node, a relatively small independent tree is built summarizing co-occurrences. Finally, a simple and non-recursive mining process reduces the memory requirements as minimum candidacy generation and counting is needed, and no communication between nodes is required to generate all globally frequent patterns.
A Hybrid Ant Colony Optimization Technique for Job-Shop Scheduling Problems
Posted by microgen 506 days ago (Editorial)
The job-shop scheduling problem is concerned with allocating limited resources to operations over time. Although the job shop scheduling has an important role in various fields, it is one of the most difficult problems in combinational optimization. In this paper, we propose a new scheduling technique that combines
Ant Colony Optimization (ACO) with GT method in order to realize effective scheduling. ACO approach has been applied recently to several combinational optimization problems, e.g., TSP and scheduling problem. However, no studies have ever seen the
approach of applying hybrid ACO to job-shop problems. Experimental results using benchmark data show improvement comparison with a conventional scheduling technique.
Ant Colony Optimization (ACO) with GT method in order to realize effective scheduling. ACO approach has been applied recently to several combinational optimization problems, e.g., TSP and scheduling problem. However, no studies have ever seen the
approach of applying hybrid ACO to job-shop problems. Experimental results using benchmark data show improvement comparison with a conventional scheduling technique.
Maximizing the Efficiency of Parallel Apriori Algorithm
Posted by microgen 506 days ago (Editorial)
Abstract - In this paper we attempt to maximize the efficiency of the parallel Apriori Algorithm. The paper analyzes the performance of the algorithm over different datasets and over n processors on a commodity cluster of machines. In the Apriori Algorithm all processes need to synchronize after every pass. If any process is assigned more load than other processes in the system, the slowest process will dictate the speed of the program. It is therefore important to ensure that load is equally balanced among all processes. Our algorithm determines the no. of
running processes and divides the load equally so as to maximize the system performance and its efficiency. The experiments conducted show that the parallel algorithm scales well to the number of processes and also improves on the efficiency by effective load balancing.
running processes and divides the load equally so as to maximize the system performance and its efficiency. The experiments conducted show that the parallel algorithm scales well to the number of processes and also improves on the efficiency by effective load balancing.
A Parallel Apriori Algorithm for Frequent Itemsets Mining
Posted by microgen 506 days ago (Editorial)
Finding frequent itemsets is one of the most investigated fields of data mining. The Apriori algorithm is the most established algorithm for frequent itemsets mining (FIM). Several implementations of the Apriori algorithm have been reported and evaluated. One of the implementations optimizing the data structure with a trie by Bodon catches our attention. The results of the Bodon’s implementation for finding frequent itemsets appear to be faster than the ones by Borgelt and Goethals. In this
paper, we revised Bodon’s implementation into a parallel one where input transactions are read by a parallel computer. The effect a parallel computer on this modified implementation is presented.
paper, we revised Bodon’s implementation into a parallel one where input transactions are read by a parallel computer. The effect a parallel computer on this modified implementation is presented.
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