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Journal of software incremental updating algorithm

Algorithms for calculating variance play a major role in computational statistics.

A key difficulty in the design of good algorithms for this problem is that formulas for the variance may involve sums of squares, which can lead to numerical instability as well as to arithmetic overflow when dealing with large values.

can be very similar numbers, cancellation can lead to the precision of the result to be much less than the inherent precision of the floating-point arithmetic used to perform the computation.

Thus this algorithm should not be used in practice.

This is particularly bad if the standard deviation is small relative to the mean.

Techniques such as compensated summation can be used to combat this error to a degree.It is often useful to be able to compute the variance in a single pass, inspecting each value only once; for example, when the data are being collected without enough storage to keep all the values, or when costs of memory access dominate those of computation.For such an online algorithm, a recurrence relation is required between quantities from which the required statistics can be calculated in a numerically stable fashion.The following formulas can be used to update the mean and (estimated) variance of the sequence, for an additional element x These formulas suffer from numerical instability, as we are repeatedly subtracting a small number from a big number which scales with n.A better quantity for updating is the sum of squares of differences from the current mean, This algorithm is much less prone to loss of precision due to catastrophic cancellation, but might not be as efficient because of the division operation inside the loop.

For a particularly robust two-pass algorithm for computing the variance, one can first compute and subtract an estimate of the mean, and then use this algorithm on the residuals.

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Software evolves from one version to another in its life span. The effort of mining frequent sequential patterns across multiple versions of a software can be substantially reduced by efficient incremental mining. This problem is challenging in this domain since the databases are usually updated in all kinds of manners. 
10-Feb-2019 04:34
International Journal of Database Theory and Application. Vol.9, No.4 2016, pp. 195-208. The incremental mining algorithm is based on Apriori, and FIUA 5 is based on frequent pattern tree incremental updating algorithm of association rules. FIUA exploited original frequent itemsets and original FP-trees to efficiently. 
10-Feb-2019 04:39
Journal of Software 软件学报 2002,131 an almost quadratic time. we present an incremental clustering algorithm——IGDCA based on GDCA, dealing with a bulk of updates. The rest of the paper is organized as follows In section 1, we present a grid density-based clustering algorithm. ——GDCA followed by some. 
10-Feb-2019 04:41
Techniques use a bug reports to query a software repository to retrieve relevant. LAST COLUMN. SHOWS THE % REDUCTION IN RETRIEVAL ACCURACY. Software. Model. BugID. Batch. Mode. Limited. Update. %. Re- duction. JodaTime. SUM. update the index and the incremental algorithms to update the model. 
10-Feb-2019 04:45
Dec 20, 2017. DOI 10.1007/978-3-642-22577-2_46. Cite this publication. Sanjay Chakraborty at Institute of Engineering & Management. Sanjay Chakraborty. 23.61; Institute. In incremental approach, the K-means clustering algorithm is applied to a dynamic database where the data may be frequently updated. And this. 
10-Feb-2019 04:50
Optimization Methods and Software 324, 963-992. 2017 ϵ-subgradient algorithms for bilevel convex optimization. Inverse Problems 335, 055020. 2017 Multitask Diffusion Adaptation Over Networks With Common Latent Representations. IEEE Journal of Selected Topics in Signal Processing 113, 563-579. 
10-Feb-2019 04:53

Journal of software incremental updating algorithm introduction

Journal of software incremental updating algorithm

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