Deploying Artificial Intelligence in Service Delivery in Educational Institutions

Abstract

In the world of data mining, the k-means clustering algorithm is regarded as one of the most effective and well-liked methods. Although the approach is widely used, it does have certain drawbacks, such as issues with centroids' random initialization, which might result in unforeseen convergence. Moreover, the number of clusters that must be determined in advance for this type of clustering method is what determines the distinct cluster forms and outlier effects. The inability of the k-means algorithm to accommodate different data formats is a basic issue. This work used Halstead Complexity measure to find the software complexity of k- means algorithm. K-Means algorithm was written in C++, C#, and Java programming language. The software complexity of C++, C#, and Java programming language was evaluated using Halstead Complexity measure. The result obtained was compared in order to discover the complexity of all the different implementation languages. Three different codes of K-means algorithm were written in C++, C# and Java programming language. Halstead complexity measure was used to evaluate the different implementation structures of programming languages for comparative analysis of complexity measure. Comparatively, the results showed that Java programming language performed better than C++ and C# in vocabulary of program, estimated program level, effort to generate program and programming time. In this work, it was discovered that Java has the smallest elementary mental discrimination time to construct a program which is 15.426 seconds when compare to the others. Key information about software testability, dependability, and maintainability may be predicted using complexity measurements from computerized source code assessment. Keywords: Clustering Algorithm, Complexity, Convergence, Source Code, Software, Vocabulary Lala, O.G., Onamade, A.A., Oduwole, O.A., Sunday, P., Aroyehun, A.A. & Olabiyisi, S.O. (2022): Performance Evaluation Of The Effect Of Implementation Languages On The Sofware Complexity Of K- Means Algorithm. Journal of Advances in Mathematical & Computational Sciences. Vol. 9, No. 2. Pp 61-74. DOI: dx.doi.org/10.22624/AIMS/MATHS/V10N2P6 Available online at www.isteams.net/mathematics-computationaljournal.