Data Mining on Stock Data Effective Decision Making for Future Stock

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 3 | Issue 01 | June 2016 ISSN (online): 2349-6010

Data Mining on Stock Data Effective Decision Making for Future Stock Sonia Sawale PG Student Department of Computer Science & Engineering RKDF University, Agnoss College of Technology, Bhopal, 462033, Madhya Pradesh

Dinesh Sahu Professor Department of Computer Science & Engineering RKDF University, Agnoss College of Technology, Bhopal, 462033, Madhya Pradesh

Amitesh Paul Professor Department of Computer Science & Engineering RKDF University, Agnoss College of Technology, Bhopal, 462033, Madhya Pradesh

Abstract 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.

Keywords: K-Means, Data Mining _______________________________________________________________________________________________________ I.

INTRODUCTION

Now a days data runs in to giga byte and tera byte range in future the same data may start belong to peta byte range. Since the data is fundamental to every organization.

As from the abundance of resource we need to extract the meaningful data which is required for our day to day decision making.

Manually extracting data on daily basis is not possible there has to be involvement of machine to analyze it and put up for decision.

For having sales forecasting the stocks can be arrange and can be grouped in to different categories and based on groups the forecast to be formalized II. ANALYSIS Data mining researchers often try to find most feasible and efficient methods for extraction of useful patterns from stock data.

Most of the research regarding stock data mining uses the history of transactions as it likely that may persist in future. These can help to predict the customer behavior and future trend.

III. IMPLEMENTATION Our implemented approach is a two phased model. First we generate clusters using K-Mean algorithm, and then MFP is designed for counting frequencies of items under their specified attributes.. In phase-1 the first step is to collect sample data from real store inventory data. We have process the data to remove the noise first, so the incomplete, missing and irrelevant data are removed and formatted according to the required format.

K-Means K-means clustering algorithm and has used for classification of data. Proximity is usually measured by some sort of distance; the most commonly being used is the Euclidean distance.

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Data Mining on Stock Data Effective Decision Making for Future Stock (IJIRST/ Volume 3 / Issue 01/ 036)

Most Frequent Pattern (MFP) Association rule mining is one of the most important and well defines technique for extract correlations, frequent patterns, associations or causal structures among sets of items in the transaction data Jules or other repositories. Association rules are widely used in various areas such as risk management, telecomm, market analysis, inventory control, and stock data. Apriori algorithm for strong association among the patterns is highly recommended. In this work we proposed a new algorithm MFP that is more efficiently generates frequent patterns and strong association between them. For this purpose a property matrix containing counted values of properties of each product has been used as shown in Figure 2.Let we have set X of N items in a Dataset having set Y of attributes. This algorithm counts maximum of each attribute values for each item in the dataset.

Product Clustering

Fig. 1: Product Clustering

Cluster

Fig. 2: Cluster

Color Based Prediction

Fig. 3: Color Based Prediction

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Data Mining on Stock Data Effective Decision Making for Future Stock (IJIRST/ Volume 3 / Issue 01/ 036)

Gender Based Prediction

Fig. 4: Gender Based Prediction

Seasons Based Prediction

Fig. 5: Seasons Based Prediction

IV. RESULT & CONCLUSION In this project the problem of pattern discovery from stock data mining is addressed. Hybrid clustering association mining approach is done to classify stock data and find compact form of associated patterns of sale.

We are able to formulate most frequent pattern of products using their known properties in inventory system. We have identified the trends of selling products through their known attributes. Our technique is simple by using matrix and counting of attribute values.

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Darken, C. Moody, J. Yale Comput. Sci., New Haven, “ Fast adaptive k-means clustering” IEEE- 2002 L.K.Soon, Sang Ho Lee, “An Emperical Study of Similarity Search in Stock Data” Australian Computer Society, Second International Workshop on Integrating AI and Data Mining, AIDM-2007. pp 31-38

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