An efficient algorithm for mining sequential

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An efficient algorithm for mining sequential

O 1 In computer sciencebinary search, also known as half-interval search, [1] logarithmic search, [2] or binary chop, [3] is a search algorithm that finds the position of a target value within a sorted array.

An efficient algorithm for mining sequential

If they are not equal, the half in which the target cannot lie is eliminated and the search continues on the remaining half, again taking the middle element to compare to the target value, and repeating this until the target value is found. If the search ends with the remaining half being empty, the target is not in the array.

Even though the idea is simple, implementing binary search correctly requires attention to some subtleties about its exit conditions and midpoint calculation, particularly if the values in the array are not all of the whole numbers in the range. Binary search runs in logarithmic time in the worst casemaking O log n comparisons, where n is the number of elements in the array, the O is Big O notationand log is the logarithm.

Binary search takes constant O 1 space, meaning that the space taken by the algorithm is the same for any number of elements in the array.

Although specialized data structures designed for fast searching, such as hash tablescan be searched more efficiently, binary search applies to a wider range of problems.

There are numerous variations of binary search. In particular, fractional cascading speeds up binary searches for the same value in multiple arrays. Fractional cascading efficiently solves a number of search problems in computational geometry and in numerous other fields. Exponential search extends binary search to unbounded lists.

The binary search tree and B-tree data structures are based on binary search.RESEARCH ARTICLES Enhancement of Critical Parameters of Natural Ester Liquids Using SiO 2 Insulating Nanoparticle M.

Srinivasan, U. S. Ragupathy, and A. Raymon J.

An efficient algorithm for mining sequential

Comput. Theor. We propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step.

Abstract. Mining sequential rules are an important problem in data mining research. It is commonly used for market decisions, management and behaviour analysis.

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In traditional association-rule mining, rule interestingness measures such as conīŦdence are used for determining relevant knowledge. new algorithm. An experimental study is presented in Section 6.

Section 7 discusses how the sequence mining can be used in a realistic domain.

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Finally, we conclude in Section 8. 2.

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Problem statement Theproblemofminingsequentialpatternscanbestatedasfollows: Let IDfi1;i2;;img be a set of m distinct items comprising the alphabet.

International Journal of Innovative Computing, Information and Control (IJICIC) ISSN Contents. To improve the sequential pattern mining efficiency, we proposed an efficient mining algorithm, SPMW, to discover frequent sequential patterns with flexible wildcards and one-off conditions.

American Scientific Publishers - Journal of Computational and Theoretical Nanoscience