Data mining is a descriptive and predictive data analytical technique that discovers meaningful and useful knowledge from dataset. Clustering is one of the descriptive analytic techniques of data mining that uses latent statistical information that exists among dataset to group them into meaningful and or useful groups. In clinical decision making, information from medical tests coupled with patients’ medical history is used to make recommendations, and predictions. However, these voluminous medical datasets analysis is always dependent of individual analyzer that might have in one way or the other introduced human error. In other to solve this problem, many automated analyses have been proposed by researchers using various machine learning techniques and various forms of dataset. In this paper, dataset from electrical impedance imaging of breast tissues are clustered using two unsupervised algorithms (k-means and self-organizing map). Result of the performances of these machine learning algorithms as implemented with R i368 version 3.4.2 shows a slight outperformance of K-means in terms of classification accuracy over self-organizing map for the considered dataset.

Retrieval in existing e-recruitment system is on exact match between applicants' stored profiles and inquirer's request. These profiles are captured through online forms whose fields are tailored by recruiters and hence, applicants sometimes do not have privilege to present details of their worth that are not captured by the tailored fields thereby, leading to their disqualification. This paper presents a 3-tier system that models serialized documents of the applicants' worth and they are analyzed using document retrieval and natural language processing techniques for a human-like assessment. Its presentation tier was developed using java server pages and middle tier functionalities using web service technology. The data tier models résumés that have been tokenized and tagged using Brill Algorithm with my sequel. Within the middle tier, indexing was achieved using an inverted index whose terms are noun phrases extracted from résumés that have been tokenized and tagged using Brill Algorithm.

How to manage patient's service time has been a burden in view of the condition of patients who have to wait for required service from doctors in many hospitals in developing countries. This paper deals with the management of service time of doctor's consultation using parallel service time. Though, the theoretical underlying distribution of service time is exponential, but this research showed service time to be non-exponential but, normal. This unusual distribution of service time was attributed to non-identical services required by patients from doctors in the considered sample space. Secondly, the mean service time from each service point as researched was found the same. This showed that no line could be preferred to other.