Data Mining and Predictive Analysis

Running head: DATA MINING AND PREDICTIVE ANALYSIS 1
Data Mining /Analyze technique to Discover Logic
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DATA MINING AND PREDICTIVE ANALYSIS 2
Data mining is a wide field that involves analyzing large information of data sets and
identifying some patterns in them using statistical algorithms to come up with new information
or new meaning from the existing one (Liu, 2012). Data mining can be mistakenly thought to be
a method of extracting new information due to the word ‘mining’ but it rather refers to the using
of existing data to generate new ones.
Predictive analysis on the other hand covers the whole data mining processes and other
techniques like artificial intelligence, machine learning, statistics and modelling to make
prediction about the future.
Data sets in data mining are important for retrieving and obtaining new information since
they are collection of information that are related. These data sets are available for either public
use or for private analysis only (Wu et al, 2013). Data sets are stored in databases and in this
study, information about certain public databases are going to be analyzed and identify ways in
which data mining tools help flag data that do not meet review or human intervention.
Information from data sets in public databases is bulk but to be specific, data from the US
governments’ open data is going to be used for the data mining analysis. One scenario built in
public databases include Open data revolution to fight global hunger in the US government’s
open data website.
According to the scenario, there is a lot of information about various aspects of human
life’s. These information is use to make informed decisions and hence mostly the decisions made
are accurate and effective. In the area of food and agriculture, there is not enough information
and data sets like in the area of weather forecasting that enable prediction of accurate weather
forecast.

DATA MINING AND PREDICTIVE ANALYSIS 3
The United States Department of Agriculture (USDA), a firm that oversees farming
industry in America recognizes agriculture practitioners like ranchers and farmers as consumers
also. This is because they use data daily to make decisions for their practices. Deciding when to
plant, when to harvest or when to take their animals for pastures, requires prior information for
accurate decision making.
To flag data involves identifying data for a specific purpose, that is identifying data
because it meets certain query requirements. Methods of flagging data using data mining tools
include the techniques that are involved in data mining.
These techniques are like clustering, classification, association rules, prediction,
sequential patterns and outlier detection. Classification involves classifying data and information
into different classes (Maroco, 2011). The purpose of classification in flagging is to acquire data
and metadata relevant to for review but lacks logic guidelines for human understanding and
intervention. Another way of flagging data involves clustering. Clustering entails identifying
data that has similarities and grouping them together. by doing this review of information and
data can be made without human intervention
Regression analysis is another important way of flagging data. Regression comprises the
process of identifying and coming up with conclusion about variables. One variable is used to
predict the behavior and outcome of another variable.
Prediction is another area that involves identifying data for a specific purpose. Prediction
combines several other techniques in data mining like clustering, classification and sequential
patterns. All these techniques involve having past events or instances that help in predicting the
future.