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Handling Uncertain Data By Using Decision Tree Algorithms 1 Aarati R. Patil, 2Nilesh V. Patil, 3Kamini S.Patil 1 BE Student of NMU, Jalgaon, AP-India,[email protected] 2 BE Student of NMU, Jalgaon, AP-India,[email protected] 3 BE Student of NMU, Jalgaon, AP-India,[email protected] 1 Naive Bayes, Neural Networks and Decision Tree algorithms. Abstract-- Traditional decision tree classifiers work with data whose values are known and precise. The proposed approach extends such classifiers to handle data with uncertain information. There are sources of uncertain data includes measurement analysis errors, data staleness and repeated measurements. This approach extends classical decision tree building algorithms to handle data tuples with uncertain values. Since processing PDFs is computationally more costly than processing single values (e.g., averages), decision tree construction on uncertain data is more CPU demanding than that for certain data. To tackle this problem, this is the proposed series of pruning techniques that can greatly improve construction efficiency. Index Terms-- Data Mining, Uncertain Data, Classification, Distribution, Averaging, Decision Tree, Pruning Techniques. One of the important method in classification is called decision tree. It can be defined as it is structured tree where each node represents a test on a attribute value as well as every branch i.e., age represents an output of the test. The leaves in the tree are represented as classes. Models of decision trees can be characterized into 2 ways, first is descriptive and predictive is another one. Decision tree as respective to data mining having of 2 types of trees, classification & regression trees. Classification tree is used, when the output(which is predicted) is the class in which the data belongs. The paper contains introduction of data mining, classification, decision tree and background. Motivational Theory with the objectives, literature survey and reviews done for the proposed approach are described in the paper. The Proposed system and implemented algorithms as well as I. INTRODUCTION Results are described in next chapters of paper. Data uncertainty is generally used in emerging applications. Data uncertainty can be caused by multiple factors which II. MOTIVATION consists measurements precision limitations, derived from The certain data can be defined as the data which having applications due to different reasons such as outdated values that never changes. That means certain data is sources & imprecise measurements. Now a days, huge always fixed by certain means. The values associated with amount of historical data handling is very important. The certain(fixed) data sets are fixed, they never changes their data mining is very effective tool which extracts knowledge attribute values. What is the need of analysis of the from stored historical information. Classification is one of performance of this certain data set? Because these data the most effective techniques in data mining to handle an sets contains historical data so we need to keep it in uncertain data. Classification means separation or ordering proper way, by this means it is helpful for users of it. The of objects into classes. It involves 3 data mining techniques performance analysis of certain data is quite easy task. But the problem arises when the term uncertain comes in application. The uncertain data means the data which changes randomly day by day, user to user and even 2 (varies from)application to application. The values current behavior or predict future outcome. Data Mining associated with the uncertain data set and they are process by explaining how it can be used to solve real updated as respective to their continuation. They are not problems, & then allowing to do own data mining [3]. fixed so when the user want to analyze the performance of Wei Dai and Wei Ji [4], suggested that implementation of uncertain data set it becomes difficult due to the changing Decision Tree using C4.5 algorithm, by using Map Reduce nature of data set. And the efficient analysis is needed for techniques. A decision tree is directed tree(structured tree) better performance. with edges and nodes. C4.5 uses values of information gain for tree construction [4]. A. OBJECTIVES The need to handle uncertain data is increasing day by day. The performance analysis of uncertain data is done to IV. PROPOSED SYSTEM achieve some objectives that are as follows: There is a problem of constructing decision trees classifiers 1. To implement algorithm to construct decision trees from on data with uncertain numerical attributes. The Proposed uncertain data by using averaging-method. system overcomes this drawback. The model is proposed to 2. To construct a decision tree model using distribution- achieve some goals and they are described in section 2.1. based technique. The block diagram of the proposed system is shown as 3. Even when the data which is particularly used is uncertain follows. The proposed architecture involves modules in high amount, take satisfactory results in experimentation explained as follows: by using pruning technique. A. DATA INSERTION 4. Pruning technique highly improves efficiency of building In many applications, however, data uncertainty is common. a decision tree model. With uncertainty, the value of a data item is often 5. To establish a theoretical basic on which pruning represented not by one single value, but multiple values techniques states that computational efficiency of the form is a probability distribution. The uncertain data in decision trees based on distribution-based algorithms can be module is inserted by user. significantly improved. B. GENERATE TREE Construction of a decision tree on tuples(values in datasets) with numerical, decimal point values then data is III. LITERATURE SURVEY they have computationally demanding. Given a set of n training tuples suggested that data with uncertain information, rather than with a numerical attribute, there are binary split points or fixed values. Data uncertainty arises due to different values ways to partition. Computationally expensiveness can in different applications. Decision tree is made by averaging obtained by finding the best split point. Using classification, method which is more CPU demanding [1]. DT is generated. Bhosale J. D. and Patil B. M. [2], discussed that data C. AVERAGING uncertainty is very common in applications such as sensor A simple way to handle data uncertainty is to abstract networks by various factors like probability distributions by summary statistics such as measurement errors, outdated sources. Classification is the means and variances. This approach is called Averaging. most popular data mining technique which includes Averaging is a greedy algorithm that builds a tree top-down. averaging and distribution method [2]. When processing a node, examine a set of tuples S. The Richard J. Roiger and Michael W. Geatz [3], studied that the working of C4.5 algorithm starts with the root node and with According to Sunit S. Dongare et. al., [1], etc. and caused data mining is discovery of patterns in data to help explain 3 S being the set of all training tuples. At each node n, first a) If the instances in subclass satisfy predefined criteria check if all the tuples in S have the same class label. of set of remaining attribute choices for this path of tree is NULL, specify classification for new instances following the decision path. (b)If the subclass does not satisfy the predefined criteria and there is at least one attribute to further subdivide the path of the tree, let T be the current set of subclass instances and return to step 2 [4]. Now, following is the Pre Pruning algorithm which eliminates unwanted nodes from tree, increases execution time as well as improves efficiencyDPSA(Decision tree pre-pruning self-learning algorithm)[1]. Input- decision table S=<U,R,V,f>, where R=CUD. Output- A decision tree. Step 1- Calculate the global certainties of the decision table Influenced by each of its condition attributes. Then select the maximum one (μc(a)), and denote the associated condition attribute as a. Step 2- Create a node by the whole instances of the current decision table, use condition attribute n to divide’ the present decision table. Each class determines one branch of Figure 1. Complete Methodology for proposed system(Block Diagram). D. DISTRIBUTION-BASED An approach is to consider the complete information carried by the probability distributions to build a decision tree. This approach is called Distribution-based. the node. Calculate the certainties of each condition attribute class, and denote them as K (Eai), where i=1,2 ,... m, m=| U/IND(u)|. Step 3- For each class do: If K (Eai) >= μc(a), or there is no other condition attribute left, then create a leaf node for this class; Else V. ALGORITHMS Following are the steps of C4.5 algorithm [4]. Step 1- Let T be the training instances or datasets. Step 2- Then select the attribute that differentiates instances in T in best way. Step 3- Then create a tree node whose value is the attribute that is previously selected. Create a child node from this selected node where each link represents a unique value for the chosen attribute. Use this child node value to further subdivide instances into subclasses. Step 4- Then for each subclass created in step3 { Generate a sub decision tables by selecting the instances corresponding to this class and deleting attribute a from the current decision table. Select the maximum global certainty μc(b) of the sub decision table influenced by each condition attribute, and denote the associated attribute as b. If μc(b) = K (Eai), then create D leaf node for Else- Let a=b, μc(a), = μc(b) take the sub 4 decision tables generated above as current decision table and important issue, because of the huge amount of information go to step 2. to be processed, as well as there is participation of more this class; complicated entropy computations. Therefore, the approach } is devised a series of pruning techniques to improve tree Step 4- Return the decision tree [1]. construction efficiency. Algorithms have been experimentally verified to be highly effective at execution VI. RESULTS OF EXPERIMENT time. Some of these pruning techniques are generalizations The developed system after executing the C4.5 algorithm of analogous techniques for handling decimal point valued decision tree of the training dataset is generated. As well as data. Other techniques such as pruning by bounding and after execution of Pruning method, the unwanted nodes of end-point sampling are also helps in increasing execution tree are eliminated from resulted tree. Following is the bar time. Although proposed model techniques are primarily graph showing the analysis of share market, which shows designed to handle uncertain data, they are also useful for prediction of next days in values of share and C4.5 construction of decision trees using classification algorithms algorithm works on that values to predict the solution. The when there are tremendous amounts of data tuples. solution helps customer to decide whether to invest or not. VII. REFERENCES 1500 1450 [1] Sunit S. Dongare et. al., “Analysis on uncertain Data of Prediction Of Shares For Seven Days Share Market using Decision Tree and Pruning Algorithm”, 1400 IJCEA, ISSN 2321-3469, Vol. VI, Issue III, June 14. 1350 [2] Bhosale J. D. and Patil B. M., “Performance Analysis on 1300 Uncertain Data using Decision Tree”, IJCA(0975-8887), 1250 Real 1200 predicted Vol. 96- No. 7, June 2014. [3] Richard J. Roiger and Michael W. Geatz, “Data Mining: 1150 A Tutorial Based Primer”, by Pearson Education. 1100 [4] Wei Dai and Wei Ji, “A Map Reduce Implementation of 1050 C4.5 Decision Tree Algorithm”, IJDTA, 2014.7.1.05, Vol 7, 1000 No.1(2014), pp. 49-60. Day Day Day Day Day Day 1 2 3 4 5 6 [5] J. R. Quinlan, “Induction of Decision Trees”, Machine Learning, vol. 1, no. 1, pp. 81-106, 1986. Figure 2. Analysis of share market for 7 days using Bar [6] C4.5- “Programs for Machine Learning”, Morgan Graph. Kaufmann, 1993, ISBN 1-55860-238-0. VI. CONCLUSION Two decision tree techniques are used, averaging and distribution-based. These techniques are based on C4.5 algorithm. In this measures are calculated in decision tree, such as information entropy and information gain. Therefore the approach is to advocate that data to be collected and stored with the PDFs information infact. Performance is an