Prediction Analysis Techniques of Data Mining

The process of extraction of interesting knowledge and patterns to analyze data is known as data mining. In data mining there are various data mining tools available which are used to analyze different types of data. Decision making, market basket analysis, production control, customer retention, scientific discovers and education systems are some of the applications that use data mining in order to analyze the collected information (Bellaachia and Guven, 2010). The customer categorized group and purchasing patterns done by clustering can be used by marketer to discover their customer‘s interest. In biology, it can be used to derive plant and animal taxonomies, categorize genes with similar functionality, and gain insight into structures inherent in populations. In a city, similar houses and lands area can be identified by employing clustering in geology. To discover new theories, information clustering can be used that classify all documents available on Web. The unsupervised data clustering classification method create clusters, group of objects in such a way that objects in different clusters are distinct and that are in same cluster are very similar to each other. In data mining, cluster analysis is considered as one of the traditional topics that is the first step in discovery of knowledge. The data objects are grouped into a set of disjoint classes which is known as clusters (Oyelade, et. al., 2010). Now, objects within a class have high resemblance to each other and in the meantime objects in separate classes are more unlike. Following are some broader categories into which the clustering methods have been categorized:

The distance amongst the objects is taken as a base in order to separate the objects into clusters in most of the technique. However, these methods can only be helpful while identifying the spherical shaped clusters. It is difficult to obtain arbitrary shaped clusters within these techniques.

A grid structure is generated by quantizing the object space into finite number of cells which is known as grid based method. This method has high

The group membership for data instances can be predicted with the help classification technique within the data mining. In order to predict the data for example classification can be utilized by the applications on a specific day to identify the weather which can be either ―sunny‖, ―rainy‖ or ―cloudy‖. Two steps are followed within this process. They are (Rauf, et. al., 2012): 1.5.1 Model Construction:

Second step used in the classification is model usage. In order to classify the future and unknown objects model usage is widely used as this model estimates the accuracy of the model. The classified result from the model is used to compare with the known label of test sample. Test set is not dependent on training set.

SVM stands for support vector machine. It is a binary classifier that maximizes the margin. The best hyperplane which separates all the data points of an individual class can be identified through the classification provided by SVM. The largest margin between the two classes describes the best hyperplane for an SVM (Kajal, et. al., 2013). The maximum width between the slabs parallel to the hyperplane is known as margin which has no interior data points. The svm algorithm is used to separate maximum margin in hyperplane. The margin planes determined using the point from each class are called support vectors (SVs). It has many applications such as bioinformatics, text, image recognition etc. It becomes popular due to its success in handwritten digit recognition. A huge and varied community works on them like machine learning, optimization, statistics, neural networks, functional analysis, etc.

Min Chen, et.al proposed in this paper [7], a novel convolutional neural network based multimodal disease risk prediction (CNN-MDRP) algorithm. The data was gathered from a hospital which included within it both structured as well as unstructured types streamlined here. 94.8% of prediction accuracy was achieved here along with the higher convergence speed in comparison to other similar enhanced algorithms.

Chew Li S. et.al, (2013) presented in this paper [10] particular university student results has been recorded to keep a track using Student Performance Analysis System (SPAS). The design and analysis has been performed to predict student‘s performance using proposed project on their results data. The data mining technique generated rules that are used by proposed system to gives enhanced results in predicting student performance. The student‘s grades are used to classy existing student using classification by data mining technique. Qasem A. et.al, (2013) presented in this paper [11] that data analysis prediction is considered as import subject for forecasting stock return. The data analysis future can be predicted through past investigation. The past historical knowledge of experiments has been used by stock market investors to predict better timing to buy or sell stocks. There are different available data mining techniques out of all a decision tree classifier has been used by authors in this work. K.Rajalakshmi et.al, (2015) presented in this paper [12] a study related to medical fast growing field authors. In this field every single day a large amount of data has been generated and to handle this much of large amount of data is not an easy task. The medical line prediction based systems optimum results are produced by medical data mining. The K-means algorithm has been used to analyze different existing diseases. The cost effectiveness and

prediction system based data mining. Bala Sundar V et.al, (2012) examined in this paper [13] real and artificial datasets that have been used to predict diagnosis of heart diseases with the help of a K-mean clustering technique results to check its accuracy. The clusters are partitioned into k number of clusters by clustering which is the part of cluster analysis and each cluster has its observations with nearest mean. The first step is random initialization of whole data then a cluster k is assigned to each cluster. The proposed scheme of integration of clustering has been tested and its results show that highest robustness, accuracy rate can be achieved using it. Daljit Kaur et.al (2013) explained in this paper explained [14] that data contained similar objects has been divided using clustering. A data of similar objects are in same group and in case dissimilar objects occur then it will be compared with other group‘s objects.. The proposed algorithm has been tested and results shows that it is able to reduce efforts of numerical calculation, complexity along with maintaining an easiness of its implementation. The proposed algorithm is also able to solve dead unit problem.

The prediction analysis is the technique which can predict the future possibilities from the existing data. The prediction analysis techniques are based on the clustering and classification. The CNN-MDRP modal for the prediction analysis is based on the neural networks. In which the clustering algorithm called k-means clustering is applied which can categorize the data into certain number of classes. The clustered data is given as input to the classification algorithm which can divide the dataset into two parts testing the data into certain number of classes. In the k-mean clustering algorithm, the centered points are calculated by taking arithmetic mean of the whole dataset which can reduce accuracy of prediction analysis. When the dataset is complex, it is difficult to establish relationship between the attributes of the dataset. In this research work, improvement in the k-mean clustering will be applied which can select centered points in the efficient manner to categorize input data. The proposed improvement directly increase accuracy of clustering and reduce execution time.

In this paper, it is concluded that prediction analysis is the technique of data mining which is used to predict future from the current data. The prediction analysis is the combination of clustering and classification. The clustering algorithm group the data according to their similarity and classification algorithm can assign class to the data. In this paper, various prediction analysis algorithms are reviewed and analyzed in terms of various parameters. The litarture survey is done on the various techniques of prediction analysis from where problem is formulated. The formulated problem can be solved in future to increase accuracy of prediction analysis.

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Ph.D. Scholar, UIET, M.D.U., Rohtak