A New Algorithm to Find The Kth Smallest Element in an Unordered List (Efficient for Big Data)

Finding the k^th smallest element in an unsorted list of values is of vital importance in several disciplines and applications such as image and signal processing, machine learning, feature extraction, statistics, trend analysis, genome sequencing and many others. Different algorithms that find the k^th smallest element in an unsorted list of values were presented in literature and used in industry. The speed and utilization of memory (time and space) are considered as the main factors on which researcher depend to select one of the algorithms instead of the other. This paper presents a proposed algorithm that is developed to find the k^th smallest element in an unsorted array of elements. The proposed algorithm is compared with other five well-known efficient algorithms (quick sort, merge sort, radix sort, counting sort, and quick-select algorithms). The outcome indicates that the proposed algorithm exceeds the other algorithms in terms of speed and memory utilization. The proposed algorithm achieves more superiority when it is applied over large sets of data which makes this algorithm competent for big data.