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Sorting is one of the fundamental issues in computer science. Sorting problem gain more popularity, as efficient sorting is more important to optimize other algorithms e.g. searching algorithms. A number of sorting algorithms has been proposed with different constraints e.g. number of iterations (inner loop, outer loop), complexity, and CPU consuming problem. This paper presents a comparison of different sorting algorithms (Sort, Optimized Sort, Selection Sort, Quick Sort, and Merge Sort) with different data sets (small data, medium data, and large data), with Best Case, Average Case, and worst case constraint. All six algorithms are analyzed, implemented, tested, compared and concluded that which algorithm is best for small, average, and large data sets, with all three constraints (best case, average case, and worst case).
This paper introduces a new hybrid algorithm that utilizes three algorithms: quicksort, merge sort, and selection sort in order to speed the sorting process for large arrays. The proposed new algorithm is called the "Three-in-One Sort". It works by first splitting an array of elements into sub-arrays, and then applying quicksort to each of the resulting sub-arrays in parallel and then merging them back in parallel into a single array. This paper proves that an unsorted array of large elements can be sorted quicker by using the proposed new Three-in-One Sort algorithm than by using the quick sort algorithm or merge sort algorithm alone. This paper goes on to present a comparison between the proposed new algorithm and the following three main algorithms (and their variants) in order to compare efficiency: quicksort, merge sort, and selection sort. The results showed that the new proposed algorithm is more efficient on large arrays by a significant amount of time.
Three-in-One Sorting Algorithm
This paper introduces a new hybrid algorithm that utilizes three algorithms: quicksort, merge sort, and selection sort in order to speed the sorting process for large arrays. The proposed new algorithm is called the "Three-in-One Sort". It works by first splitting an array of elements into sub-arrays, and then applying quicksort to each of the resulting sub-arrays in parallel and then merging them back in parallel into a single array. This paper proves that an unsorted array of large elements can be sorted quicker by using the proposed new Three-in-One Sort algorithm than by using the quick sort algorithm or merge sort algorithm alone. This paper goes on to present a comparison between the proposed new algorithm and the following three main algorithms (and their variants) in order to compare efficiency: quicksort, merge sort, and selection sort. The results showed that the new proposed algorithm is more efficient on large arrays by a significant amount of time.
CRIME PREDICTION USING CLASSIFICATION RULE MINING
2018
Data mining produce workable significant information from a huge amount of discrete data. Data mining is the process of naturally looking huge stores of information to find patterns and prediction that go past basic examination. Data mining also assumes an imperative part as far as prediction and analysis [1]. We are using here three different classification algorithm in Weka. The algorithm we have used here Naive Bayes, Bayes Net, One-R. We have collected data from Dhaka metropolitan police, Chittagong metropolitan police, Bangladesh police, Newspaper and due to sensitive data from a secret agent. The main objective of this paper is to predict place from crime occurred in the last few years. The law enforcement agencies ought to in this way have the capacity to anticipate such increments or abatements or patterns in crime for example, the number of murder, robbery, burglaries or any such crime that may happen in a specific zone in a specific month, year, or any time span or the general number of crime happening in a nation in a specific year later on, or any other expectation or projection of future crime measurements [2]. So that the law enforcement agency can find out the place easily where the crime rate is high to take proper action, thus reducing the crime rate.
List Sort A New Approach for Sorting List to Reduce Execution Time
—In this paper we are proposing a new sorting algorithm, List Sort algorithm, is based on the dynamic memory allocation. In this research study we have also shown the comparison of various efficient sorting techniques with List sort. Due the dynamic nature of the List sort, it becomes much more fast than some conventional comparison sorting techniques and comparable to Quick Sort and Merge Sort. List sort takes the advantage of the data which is already sorted either in ascending order or in descending order.
List Sort: A New Approach for Sorting List to Reduce Execution Time
Arxiv.org, 2013
In this paper we are proposing a new sorting algorithm, List Sort algorithm, is based on the dynamic memory allocation. In this research study we have also shown the comparison of various efficient sorting techniques with List sort. Due the dynamic nature of the List sort, it becomes much more fast than some conventional comparison sorting techniques and comparable to Quick Sort and Merge Sort. List sort takes the advantage of the data which is already sorted either in ascending order or in descending order.
Relative Split and Concatenate Sort (RSCS-V1)
ijcte.org
Computational problems have significance from the early civilizations. These problems and solutions are used for the study of universe. Numbers and symbols have been used for mathematics, statistics. After the emergence of computers the number and objects needs to be arranged in a particular order either ascending and descending orders. The ordering of these numbers is generally referred to as sorting. Sorting has many applications in computer systems, file management, memory management. Sorting algorithm is an algorithm by which elements are arranged in a particular order following some characteristic or law. A number of sorting algorithms have been proposed with different time and space complexities. In this research author develop a new sorting technique to keep in view the existing techniques. Author also proposed the algorithm i.e. Relative Split and Concatenate Sort, implements the algorithm and then compared results with some of the existing sorting algorithms. Algorithm's time and space complexity is also being the part of this paper. With respect to complexity sorting algorithms mainly can be divided into 2 categories: O(n2) and O(nlogn). The proposed algorithm Split and Concatenate Sort is under the category of O(n2) and is efficient, in terms of time complexity, than existing algorithms lay in this category. It is discovered that the algorithms proposed in this research is relatively simpler and efficient than some of the existing well known sorting algorithms i.e. bubble sort, insertion sort and selection sort.