The rate but some others have smaller. When

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, we proposed a
framework that is used K-modes clustering technique as an initial work for
division of various types of road accidents on road network. Then association rule
mining are used to recognize the different situations that are related with the
occurrence of an accident for both 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 information that can be related with an
accident. More information can be identified if more feature are available that
is associated with an accident. To buildup our methodology, we also performed
tendency analysis of all clusters and EDS on monthly and hourly basis. The
results of  analysis  assist methodology that performing clustering
prior to analysis helps in identify better and useful results that we cannot
obtained without using cluster analysis

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