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Title |
Predictive Sort |
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Author |
Anupam Shukla, Rahul Kala |
| Citation |
Vol. 8 No. 6 pp. 314-320 |
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Abstract |
We know the various sorting algorithms available today. Sorting has become one of the most essential parts of the artificial intelligence algorithms these days. We have so many algorithms like Quick Sort, Hash Sort, Bucket Sort, Radix Sort, Insertion Sort, etc. All these are applied to various problems in their own way. In this paper we present a new sorting algorithm. This algorithm works on the principle of calculating the position of each and every node and then placing it onto that position. The algorithm does not start sorting on an extreme end and march towards the other end, as the other algorithms. Also it does not use a divide and conquer approach. Because in both these approaches, after half time of sorting, one half is completely sorted, but the other half is completely unsorted. Here we consider the case where a huge data is to be sorted. Our algorithm sorts this data evenly with respect to time. Hence after half of the time of algorithmic run, we can predict the appropriate position of any of the node. The basic applicability of such an algorithm lies in situations where there is no time to sort the entire data, but we need approximate positions of node in the final answer. Such an algorithm can be used for weather forecasts where usually the data is very big, or for sorting the raw data generated by GPS for fast data processing. The algorithm was implemented and tested in a random set of data. The results clearly proved the working of the algorithm. The accuracy of the algorithm improved very rapidly at each iteration. This further emphasized on the efficiency of the algorithm. This means that with a small increase in number of iterations, we may be able to get a high gain in performance. |
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Keywords |
Sorting, algorithms, partial sorting |
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