Mashiour Rahman

In this paper, we have tried to identify the prominent factors of Rice production of all the three seasons of the year (Aus, Aman, and Boro) by applying K-Means clustering on climate and soil variables' data warehoused using Fact Constellation schema. For the clustering, the popular machine-learning tool Weka was used whose visualization feature was principally useful to determine the patterns, dependencies, and relationships of rice yield on different climate and soil factors of rice production.

K-means algorithm is one of the most popular algorithms for data clustering. With this algorithm, data of similar types are tried to be clustered together from a large data set with brute force strategy which is done by repeated calculations. As a result, the computational complexity of this algorithm is very high. Several researches have been carried out to minimize this complexity. This paper presents the result of our research, which proposes a modified version of k-means algorithm with an improved technique to divide the data set into specific numbers of clusters with the help of several check point values. It requires less computation and has enhanced accuracy than the traditional k-means algorithm as well as some modified variant of the traditional k-Means.

Students' satisfaction plays a vital role in success of an educational institute. Hence, many educational institutes continuously improve their service to produce a supportive learning environment to satisfy the student need. For this reason, educational institutions collect student satisfaction data to make decision about institutional quality, but till now it cannot be determined because student satisfaction is a complex matter which is influenced by variety of characteristics of students and institutions. There are many studies have been performed to inspect student satisfaction in the form of college services, programs, student accommodation facility, student-faculty interaction, consulting hours etc. So, still we cannot have a standard method to know what is going on about satisfaction in the case of a core course. In this research we determined the attributes that heavily affect student satisfaction in a core course of computer science and the current status of other attributes as well.