Data Mining in Plant Biotech

Running head: DATA MINING IN PLANT BIOTECH
DATA MINING IN PLANT BIOTECH
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1DATA MINING IN PLANT BIOTECH
Introduction
Initially, this paper provides the basic concepts of the development of data mining and its
connection with the data warehousing. Data warehousing and data mining are the emerging
technologies for the information system and the new courses ad curricula are created revolving
these technologies.
A brief description of the plant bio-tech economic is provided. The data used in this
paper are from Osmotic Stress Microarray Information Database (OSMID). The data can be used
for the application of the genetically modified (GM) foods and plant-made-pharmaceuticals
(PMP). The data from the OSMID data warehouse is used for the findings of the economic and
environmental factors. This paper also discussed about the future direction of the study along
with the conclusion.
What is Data Mining?
Sometimes the term Data Mining is called “data knowledge discovery”. It is a process of
discovery of the information automatically. Data Mining refers to the analysis of the data from
the different perspective and the summarization of the data into the useful information. Data
Mining is an emerging technology; however, the concept of the data mining is not the old one.
At past the organizations used to use computers and other technical devices for managing the
large amount of data and the analysis of the market conditions.
According to Bigus (1996), the discovery of data mining leads an efficient way to find
out important non-obvious information from the large amount of data. It also helps in the
automated discovery relationships and new facts about the data. The data mining process is the
mechanism of gaining the core knowledge. Paper by Han and Kamber (2001) and Acxiom

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Working Paper by Segall (2003) mention the relation between data base integration, data
mining and data clearing with respect to the data warehousing along with the adoption of the
appropriate mining technique and the task relevant data to form the framework for evaluation of
the new knowledge. Data mining is the knowledge discovery which involves finding cluster,
sequencing and forecasting that can be represented to define the classified rules and patterns.
Certain models such as fuzzy logic, neural network and statistical analysis decision trees and
data visualization are used for data mining.
Data mining is an emerging technology which helps the users to find the information
without asking the specific question. This is a technology associated with statistic and artificial
intelligence. The objective of this technology is "tell me something interesting, even though I
don't know what questions to ask, and also tell me what may happen." The data used for mining
should be beyond the traditional data set. As for example- patterns for the large data base or data
ware house can e used for mining.
In order to discuss about the model and structure building for the data warehouse and
their relation to the data mining can be supported by the previous literatures written by Acxiom
Working Papers of Segall (2003) and Fish and Segall (2002), Segall (2004) and Fish and Segall
(2004) respectively.
It has also been discussed about the application of the data mining algorithm in the
medical database Segall (1984,1988,2002). Modeling of data mining functions is done using
‘linear’ and ‘non-linear’ regressions and fitting of curve. In order to define the learning rules for