BibTeX:

@article{IJIRSTV3I1150,
     title={Data Mining on Stock Data Effective Decision Making for Future Stock},
     author={Sonia Sawale, Prof Dinesh Sahu and Prof Amitesh Paul},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={1},
     pages={233--235},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I1150.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

In today’s world of fast moving consumption the analysis of inventory is very important. The pattern of consumption of inventory with respect to available and in process stock is to be always calculated from vast pool of resources. Based on the data of stocks they can be categorized as dull stock, average stock and pace stock. The stocks can be categories as dull stock, average stock and pace stock whereas Dull stock means the stock which is slowly getting consumed, average stock means which is getting consumed at a moderate speed and pace stock means which is consumed at a very fast rate. Stocks can be categories based on their consumption pattern and here k means algorithm plays an important role. K means algorithm from data mining stream can make Group of the stocks hence decision making based on pattern. Along with k means algorithm we have used most frequent algorithm also.

K-Means, Data Mining