Application of STUCCO Algorithm for Finding Contrast Sets for Agricultural Datasets

Abstract

The interplay between computer science and agriculture has led to the collection of huge amounts of information in agricultural datasets. The process of turning low level data into high level knowledge is popularly known as data mining. The field of agriculture has many applications and one important application is in terms of deriving useful patterns like characteristics of disease and varieties. Understanding the distinctions between numerous contrasting groups is a crucial issue in data analysis in order to discover new patterns. These contrasting groups can represent various item classes, such as disease or varieties for different crop groups. Contrast sets are the combinations of attributes and their values that differ meaningfully in their distribution across groups. STUCCO algorithm is a search method for mining contrast sets leading to pattern discovery. The algorithm’sapplicability for pattern detection has been demonstrated using agricultural datasets in this paper. Approach resulted in significant pattern discovery for description of soybean disease and IRIS varieties characteristics