Data Mining - Concepts and Techniques.

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Recognizing the mannerism ways to get this books computer organization and design the hardware software interface 4th fourth edition by patterson hennessy is additionally useful. You have remained in right site to begin getting this info. acquire the computer organization and design the hardware software interface 4th fourth edition by patterson hennessy join that we manage to pay for here and check out the link.

[서평]「Computer Organization and Design, The Hardware/Software Interface」

An image encryption algorithm based on chaotic system and compressive sensing

This paper proposes a novel global optimization algorithm called Political Optimizer (PO), inspired by the multi-phased process of politics. PO is the mathematical mapping of all the major phases of politics such as constituency allocation, party switching, election campaign, inter-party election, and parliamentary affairs. The proposed algorithm assigns each solution a dual role by logically dividing the population into political parties and constituencies, which facilitates each candidate to update its position with respect to the party leader and the constituency winner. Moreover, a novel position updating strategy called recent past-based position updating strategy (RPPUS) is introduced, which is the mathematical modeling of the learning behaviors of the politicians from the previous election. The proposed algorithm is benchmarked with 50 unimodal, multimodal, and fixed dimensional functions against 15 state of the art algorithms. We show through experiments that PO has an excellent convergence speed with good exploration capability in early iterations. Root cause of such behavior of PO is incorporation of RPPUS and logical division of the population to assign dual role to each candidate solution. Using Wilcoxon rank-sum test, PO demonstrates statistically significant performance over the other algorithms. The results show that PO outperforms all other algorithms, and consistency in performance on such a comprehensive suite of benchmark functions proves the versatility of the algorithm. Furthermore, experiments demonstrate that PO is invariant to function shifting and performs consistently in very high dimensional search spaces. Finally, the applicability on real-world applications is demonstrated by efficiently solving four engineering optimization problems.

Political Optimizer: A novel socio-inspired meta-heuristic for global optimization

Association Rule Mining (ARM) is one of the data mining techniques used to extract hidden knowledge from datasets, that can be used by an organization's decision makers to improve overall profit. However, performing ARM requires repeated passes over the entire database. Obviously, for large database, the role of input/output overhead in scanning the database is very significant. A popular solution to improve the speed of ARM is to apply the mining algorithm on a sample instead of the entire database. In this paper, a parameterized sampling algorithm for ARM is presented. This algorithm extracts sample datasets based on three parameters: transaction frequency, transaction length and transaction frequency-length. To evaluate its performance and accuracy, a comparison against a two-phase sampling-based algorithm is performed using real and synthetic datasets. The experimental results show that the proposed sampling algorithm in some cases outperforms two-phase sampling algorithm, and achieves up to 98% accuracy.

A new sampling technique for association rule mining

Sorting huge amounts of datasets have become essential in many computer applications, such as search engines, database and web-based applications, in order to improve searching performance. Moreover, due to the witnessed prevalence of the commercial Simultaneous Multithreaded architecture (SMT), parallel programming using multithreading becomes a dire need for efficiently using all available hardware resources for one application. In this paper, one of the efficient and quick algorithms, the Quicksort, is applied as a parallel multithreaded algorithm on SMT architecture, where virtual parallelization has been achieved using the POSIX threads (Pthreads) library. The proposed algorithm is evaluated and compared with its sequential counterpart. The obtained analytical and experimental results reveal that multithreading is a viable technique for implementing the parallel Quicksort algorithm efficiently on SMT architecture, where it has been shown both analytically and experimentally that the parallel multithreaded Quicksort algorithm outperforms the sequential Quicksort algorithm in terms of various performance metrics including; time complexity and speedup.

Performance assessment of multithreaded quicksort algorithm on simultaneous multithreaded architecture

Heuristic local search algorithms have achieved good results in tackling combinatorial optimization problems, such as Travelling Salesman Problem (TSP) One of the well-known local search algorithms is the 2-opt algorithm As a local search algorithm, 2-opt has achieved approximate optimal solutions for TSP within a reasonable time, especially for small data instances However, solving large data instances of TSP using 2-opt requires extensive computation and considerable CPU time Therefore, this paper presents a parallel version of the 2-opt algorithm, exploiting the features of Optical Transpose Interconnection System (OTIS) in solving the TSP In this paper, we present the Parallel Repetitive 2-Opt (PRTO) algorithm for solving symmetric TSP on OTIS Hyper Hexa-Cell (OTIS-HHC) and OTIS Mesh of Trees (OTIS-MOT) optoelectronic architectures We assess the performance of our algorithm analytically in terms of parallel time complexity, speedup, efficiency, cost, and communication cost on both optoelectronic architectures Furthermore, a set of simulation experiments is conducted on various instances from the standard TSP library The simulation results confirm that our algorithm is efficient regarding speedup and efficiency For instance, the PRTO algorithm achieves a speedup of 329 for 6880 cities over OTIS-HHC with 36 processors Moreover, the superiority of PRTO algorithm is shown through solving the TSP on OTIS-HHC and OTIS-MOT; its performance has been compared with the performance of the Parallel Repetitive Nearest Neighbor (PRNN) algorithm in terms of speedup, efficiency, and solution quality For example, as a best case, the PRTO algorithm has shown 34 times improved speedup over the PRNN algorithm

Parallel heuristic local search algorithm on OTIS hyper hexa-cell and OTIS mesh of trees optoelectronic architectures

An interconnection network architecture that promises to be an interesting option for future-generation parallel processing systems is the OTIS (Optical Transpose Interconnection System) optoelectronic architecture. Therefore, all performance improvement aspects of such a promising architecture need to be investigated; one of which is load balancing technique. This paper focuses on devising an efficient algorithm for load balancing on the promising OTIS-Hypercube interconnection networks. The proposed algorithm is called Clusters Dimension Exchange Method (CDEM). The analytical model and the experimental evaluation proved the excellence of OTIS-Hypercube compared to Hypercube in terms of various parameters, including execution time, load balancing accuracy, number of communication steps, and speed.

The load balancing problem in OTIS-Hypercube interconnection networks

Finding test data to cover structural test coverage criteria such as branch coverage is largely a manual and hence expensive activity. A potential low cost alternative is to generate the required test data automatically. Search-based test data generation is one approach that has attracted recent interest. This approach is based on the definition of an evaluation or cost function that is able to discriminate between candidate test cases with respect to achieving a given test goal. The cost function is implemented by appropriate instrumentation of the program under test. The candidate test is then executed on the instrumented program. This provides an evaluation of the candidate test in terms of the "distance" between the computation achieved by the candidate test and the computation required to achieve the test goal. Providing the cost function is able to discriminate reliably between candidate tests that are close or far from covering the test goal and the goal is feasible, a search process is able to converge to a solution, i.e., a test case that satisfies the coverage goal. For some programs, however, an informative cost function is difficult to define. The operations performed by these programs are such that the cost function returns a constant value for a very wide range of inputs. A typical example of this problem arises in the instrumentation of branch predicates that depend on the value of a Boolean-valued (flag) variable although the problem is not limited to programs that contain flag variables. Although methods are known for overcoming the problems of flag variables in particular cases, the more general problem of a near constant cost function has not been tackled. This paper presents a new heuristic for directing the search when the cost function at a test goal is not able to differentiate between candidate test inputs. The heuristic directs the search toward test cases that produce rare or scarce data states. Scarce inputs for the cost function are more likely to produce new cost values. The proposed method is evaluated empirically for a number of example programs for which existing methods are inadequate.

Basel A. Mahafzah

Papers

A new sampling technique for association rule mining

Performance assessment of multithreaded quicksort algorithm on simultaneous multithreaded architecture

Parallel heuristic local search algorithm on OTIS hyper hexa-cell and OTIS mesh of trees optoelectronic architectures

The load balancing problem in OTIS-Hypercube interconnection networks

Using program data-state scarcity to guide automatic test data generation