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There are several researchers who
have focused on data mining techniques, particularly classification, But a few
papers about Lunar classification. In 2, the authors presented a Bayesian
network approach for short-term solar flare level prediction based on three sequences
of photospheric magnetic field parameters extracted from Solar and Heliospheric
Observatory/Michelson Doppler Imager longitudinal magnetograms. The subjective
network structure which depicts conditional independent connections among
magnetic field parameters and the quantitative conditional likelihood tables
which decide the probabilistic esteems for every factor are found out from the
informational index. They use dimensions reduction as preprocessing technique.
Two Bayesian network models are assembled using raw sequential data (BN_R) and
feature extracted data (BN_F), respectively. The clarifications of these models
are reliable with physical examinations of specialists. The performances of the
Bayesian network model are higher than the the2 performance of the naive Bayes

Paper 3 presented how to Estimate
the availability of sunshine by Specific data mining techniques, the author
takes into consideration the requirement of the data for his paper and did
pre-process of data by selecting the feature(attribute) that needed for
clustering. so, the Number of years and Mean Rainfall attributes in millimeter
were reduced from the data set. in 3 the author uses Clustering method which
makes groups(clusters) of a similar type of data. There are different types of
clustering methods, the algorithms that 3 decide used is Simple K-Means and
Expectation Maximization algorithm which is under the partition method of
clustering. The partition method is based on the greedy heuristics in which
they are used in an iterative manner to obtain a local optimum solution.  A simple k-means algorithm uses Euclidean
distance method for distance calculation. The simple k-means method is
described as first, Select the number of clusters (k) then assume k seeds as centroids
of the k clusters. The seeds could be chosen randomly by the user if the values
of data are unknown. after that, Compute the Euclidean distance of each object
of the dataset from each of the centroids. next, allocate all objects to the
cluster if the distance between the centroid of the cluster and the object is
small. after that, Compute the centroids of the clusters by calculating the
means of attribute values of the objects in the cluster. The last step is to
stop the algorithm if the stopping criterion is met or Compute the Euclidean
distance of each object again, and continue the procedure. In this paper, the
simple k-means algorithm is computed using the open source software tool Weka.
and they use Expectation maximization (em) algorithm which works in contrast to
the simple k-means algorithm. it on the concept of assuming that the objects in
the dataset have attributes whose values are distributed and we can describe it
as Assuming the initial values then, use the normal distributions and calculate
the probability of each object belonging to the two clusters. Then, calculate
the possibility of data coming from the two clusters. Finally, iterate the
process by re-assuming the parameters and go to the normal distributions and
calculate the probability again till the stopping criterion is met. The dataset
of the monthly mean maximum and a minimum temperature of Chennai, Coimbatore,
Madurai, and Kanyakumari were gathered and simple k-means an expectation
maximization algorithm were used. The clusters formed indicated that the
maximum monthly mean temperature was recorded in the month of May and June
which implied the maximum sunshine hours in these months. The maximum in
temperature implies the maximum daylight or sunshine hours. The sunshine hours
determine the amount of solar radiation that can be acquired. The maximum
amount of sunshine is recorded in the city of Chennai when compared to the
other cities in the dataset such as Coimbatore, Madurai, and Kanyakumari. In
3 the author uses Weka environment to load data.

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