Stream Data is now more than ever highly distributed, loosely structured, increasingly large in volume and changing over time. Broadly speaking, firstly the volume of data increasing exponentially each year and secondly the speed at which the new data is being generated of distinct concept and changes over time. Stream Data is generated by number of sources. Data streaming applications are typically dealing with large amounts of data over an extended period of time. However, in most cases the user is only interested in recent data instead of the whole data set. Furthermore, stream data tends to express features of a concept drift, i.e. the data is evolving over time. This would cause algorithms which consider the whole data set with the same importance to produce distorted results. In such cases the majority of processed data would not be valid anymore. Sometimes the nature of a data stream itself requires giving up a certain amount of precision because its high volume couldn’t be processed otherwise and one would end up with no information at all. If the data distribution is stable, mining a data stream is largely the same as mining a large data set, since statistically it is easily to mine a sufficient sample. The expectations of mining data streams are finding and understanding changes, maintaining an updated model. For evolving data, two classes of problems are of particular interest: model maintenance and change detection. The goal of model maintenance is to maintain a data mining model under inserts and deletes of blocks of data. In this model, older data is available if necessary. Change detection is related to quantify the difference between two sets of data and determine when the change has statistical significance. Data streams can be seen as stochastic processes in which events occur continuously and independently from each another [1]. Querying data streams is quite different from querying in the conventional relational model. A key idea is that operating on the data stream model does not preclude the use of data in conventional stored relation, data might be transient. In this paper proposed methods are addressing Classification of balanced and unbalanced data streams by considering concept drift and data skewness. The classification accuracy depends on the selection of learning model. In data streams at the time of classification ,concept drift plays the vital role .Comparing to traditional classification data stream classification needs more accurate methods .Because traditional methods always follows the training model which may not predict the novel classes. In data streams by considering the concept drift with unsupervised learning model can predict the novel class. In the proposed methodology classification of data streams are addressed by ensemble methods with supervised learning, unsupervised learning for novel class detection to increases the accuracy of the system. A scalable and adaptable online genetic algorithm is proposed to mine classification rules for the largest data streams with concept drifts. The data skewness is addressed by considering the data level, the algorithmic level to favor the positive class.

Data Mining

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