Abstract

In recent years, advances in hardware technology have facilitated the abilityto collect data continuously. Simple transactions of everyday life such as using a credit card, a phone or browsing the web lead to automated data storage. Similarly, advances in information technology have led to large flows of data across IP networks. In many cases, these large volumes of data can be mined for interesting and relevant information in a wide variety of applications. When the volume of the underlying data is very large, it leads to a number of computationaland mining challenges. Streaming data is potentially endless of incoming data at high speed and may evolve over time.the data stream has recently emerged in response to the continuous data problem. The algorithm processing the stream has no control over the order of the examples seen, and must update its model incrementally as each example is inspected. Performance of data stream classification is measuring by involving processing speed, memory and accuracy. Also A classification algorithm must meet several requirements in order to work with the assumptions and be suitable for learning from data streams that is process an example at a time and inspect it only once; use limited amount of memory.Similar to data mining, data stream mining includes classification, clustering, frequent pattern mining etc. techniques; the special focus of this paper is on classification methods invented to handle data streams.This paper discuss two improves manners on Hoeffding tree algorithm a well-known classification data stream algorithm. Both improves is based on tie breaking parameter. The first improve named Modify Hoeffding Tree Algorithm (MHTA) and the second one named Variable Random Tie Generating Values Algorithm (VRTGVA) .

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