Enabling Lazy Learning for Uncertain Data Streams

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 Enabling Lazy Learning for Uncertain Data Streams

Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 6

Abstract

 Abstract: Lazy learning concept is performing the k-nearest neighbor algorithm, Is used to classification andsimilarly to clustering of k-nearest neighbor algorithm both are based on Euclidean distance based algorithm.Lazy learning is more advantages for complex and dynamic learning on data streams. In this lazy learningprocess is consumes the high memory and low prediction Efficiency .this process is less support to the datastream applications. Lazy learning stores the trained data and the inductive process is different until a query isappears, In the data stream applications, the data records flow is continuously in huge volume of data and theprediction of class labels are need to be made in the timely manner. In this paper provide the systematicsolution to overcome the memory and efficiency. In this paper proposed a indexing techniques it is dynamicallymaintained the historical or outdated data stream records. In this paper proposed the tree structure i.e. Novellazy tree simply called Lazy tree or L-tree.it is the height balanced tree or performing the tree traversingtechniques to maintain the trained data. These are help to reduce the memory consumption and prediction italso reduces the time complexity. L-tree is continuously absorb the newly coming stream records and discardedthe historical. They are dynamically changes occurred in data streams efficiency for prediction. They areexperiments on the real world data streams and uncertain data streams. In this paper experiment on theuncertain data streams .Our experimented uncertain data streams and real world data streams areobtained from UCI Repository.

Authors and Affiliations

Suresh. M , Dr. MHM. Krishna Prasad

Keywords

Data Streams Clustering Exemplar Lazy Learning.

EP ID EP158144
DOI -
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