Open AccessA Recruitment Big Data Approach to interplay of the Target Drugs
Author(s) -
Wael Alzyadat,
Mohammad I. Muhairat,
Aysh Alhroob,
Thamer Rawashdeh
Publication year - 2022
Publication title -
international journal of advances in soft computing and its applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.15
H-Index - 18
eISSN - 2710-1274
pISSN - 2074-8523
DOI - 10.15849/zujijasaca.220328.01
Subject(s) - big data , stability (learning theory) , computer science , discretization , set (abstract data type) , data mining , data set , cluster (spacecraft) , class (philosophy) , decision tree , computation , tree (set theory) , artificial intelligence , mathematics , machine learning , algorithm , mathematical analysis , programming language
The various model that has been used to predict, datamining, and information retrieval are useful to use through the traditional database, due to big data the prediction should derive in a different role that conduct the hidden structure data based on a stability scale to allow discovering accrue unsupervised drug data. Especially, the drug data must be understandable to analysts. Following this approach, conduct the stability drug data through computation methods are quality measurements, preprocess data, k-mean cluster, and decision tree. This approach seeks to identify the data by two dimensions (vertically and horizontally), which extrapolations, compilation, and interpretation values of the dataset while considering individual attributes. A comparison with clusters defines the set for features using balance value by K-mean algorithm to determine the k clusters that consider the set of features based on two values 0 and 1, which given the discernible between dependent and independent class target, and pinpoint the relationship among them. Keywords: Big Data, Discretize, k-mean cluster Stability, Target drug