The data. Classification in data mining methodology

The paper 1 describes the association
rule mining, its classifications and the atmospheric components like roadway
surface, climate, and light condition do not strongly influence the fatal accident
rate. But the human factors like being alcoholic or not, and the impact have
strongly affect on the fatal accident rate.  A
common mechanism to recognize the relations between the data stored in huge
database and plays a very significant role in repeated object set mining is
association rule mining algorithm. A classical association rule mining method
is the Apriori algorithm whose main aim is to identify repeated object sets to
analyze the roadway traffic data. Classification in data mining methodology focus
at building a classifier model from a training data set that is used to
classify records of unrevealed class labels. The Naïve Bayes technique is one
of the probability-based methods for classification and is based on the Bayes’
hypothesis with the probability of self-rule between every set of variables. The author applies statistics
analysis and Fatal Accident Reporting System (FARS) to solve this problem. From
the clustering result some regions have larger fatal rate but some others have
smaller. When driving within those risky or dangerous states, people take more
attention. When the task performed, data seems never to be sufficient to make a
strong choice. If non-fatal accident data, weather condition data, mileage
data, and so on are available, more test could be executed thus more advice
could be made from the data.

In paper 2, K-modes clustering
technique is a framework that is used as an initial work for division of
different road accidents on road network. Then association rule mining are used
to recognize the various situations that are related with the occurrence of an
accident for the entire data set (EDS) and the clusters recognized by K-modes
clustering algorithm. Six clusters (C1toC6) are used based on properties
accident type, road type, lightning on road and road feature identified by K
modes clustering method. On each cluster association rule mining is applied as
well as on EDS to create rules. Powerful methods with higher raise values are taken
for the inspection. Rules for various clusters disclose the situations related
with the accidents within that cluster. These rules are compared with the rules
created for the EDS and resemblance shows that association rules for EDS does not
disclose correct data that can be related with an accident. If more feature are
presented large information can be identified that is associated with an
accident. To buildup our methodology, we also performed analysis of all clusters
and EDS on monthly or hourly basis. The results of analysis assist methodology
that performing clustering prior to analysis helps to identify better and
useful results that cannot obtained without using cluster analysis.

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