SEMANTIC FEATURE ENABLED AGGLOMERATIVE CLUSTERING FOR INFORMATION TECHNOLOGY JOB PROFILE ANALYSIS
Journal Title: International Journal of Data Science and Artificial Intelligence - Year 2024, Vol 2, Issue 03
Abstract
The maintenance and implementation of computer systems are the core activities of information technology. Database administration and network architecture are also included in information technology. Professionals have access to a working environment that facilitates the setup of internal networks and the development of computer systems. There is an immediate need for a suitable approach to close the gap between supply and demand for IT workers. Extensive research into IT job profiles is crucial to meeting industry demands. Educational programs must identify the abilities that the industry requires to modernize its manufacturing. Semantic Feature-Enabled Agglomerative Clustering for Information Technology Job Profiling (SEA-IT) has been proposed to overcome these challenges. Semantic analysis is performed using a tree-like strategy. The most frequently used phrases and words from each cluster of IT professions were collected to demonstrate specific knowledge. Initially, the data from the online job posting sources will be collected and pre-processed using techniques such as stemming, normalization, text correction, removing stop words, and tokenization. Secondly, the preprocessed data can extract features using a bag of words. After feature extraction, the cluster is generated using an agglomerative algorithm to form an IT job analysis result, so that the knowledge and capabilities of IT professionals can be upgraded. The simulation findings, based on evaluation criteria and other statistical tests, demonstrated the suggested algorithm. Experiments demonstrated that SEA-IT functions well with a variety of descriptive methodologies and is independent of the dataset's dimensions.
Authors and Affiliations
B. Jaison , R. Gladys Kiruba and G Belshia Jebamalar
Keywords
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