تجزیه و تحلیل الگوی ویژه با استفاده از تکنیک های یادگیری ماشین و هوش مصنوعی برای چندین مجموعه داده های ویژه

Data mining is a process of extracting patterns from a large datasets. We are trying to uncover the data bonded features which are hard to visualize and if many feature exists for the data then it becomes difficult to analyze the data. The main aim of the scheme of work is to classify the features and over which try to classify the data sets. Machine learning is one of the techniques of Artificial Intelligence which is used for extracting valuable knowledge from large data base. Machine Learning is also used for extracting patterns, models in data. In this paper we are trying to group the data based on multi-dimensional feature classification. Clustering process makes the similar features to form into one group and or else multiple groups, here in we try to group the features which are similar and form multiple groups. The US schooling data is in the form of flat files. Classification process is performed on the raw data. Classification is performed according to hierarchical clustering. Filtration process is used in order to obtain non-zero values. The saturation points are generated by performing clustering. Based on the clusters obtained the patterns can be extracted. Attribute based classification and hierarchical clustering is performed on the data. The attributes obtained are named as income and expenses. By combining the income and expenses attributes patterns can be identified. By using some combination of the two attributes some patterns have been obtain. By performing clustering on all the combinations of each attribute we can identify the patterns. Then according to this process we find the patterns generated by the feature classification or clustering process against the data classification or clustering. This new methodology tries to give the co-relation between the data and properties of a data set and how they behave.