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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 4, April 2011 POur-NIR: Modified Node Importance Representative for Clustering of Categorical Data S.Viswanadha Raju N.Sudhakar Reddy H.Venkateswara Reddy G.Sreenivasulu C.NageswaraRaju Professor in CSE,SIT Professor in CSE Assoc. Prof. in CSE Assoc.Prof,CSE Lecturer&HOD of CS JNTUH,Hyderabad, SVCE, Tirupati VCE, Hyderabad VCE,Hyderabad SVDC,Kadapa India India India India India viswanadha_raju2004@yahoo.com Abstract - The problem of evaluating node importance also change based on time by the data drifting in clustering has been active research in present days concept [11, 16]. The clustering time-evolving data in and many methods have been developed. Most of the the numerical domain [1, 5, 6, 10] has been explored clustering algorithms deal with general similarity in the previous works, where as in categorical domain measures. However In real situation most of the cases data changes over time. But clustering this type of data not that much. Still it is a challenging problem in the not only decreases the quality of clusters but also categorical domain. disregards the expectation of users, when usually require recent clustering results. In this regard we As a result, our contribution in modifying the proposed Our-NIR method that is better than Ming- Our-NIR method which is proposed by us [17] Syan Chen proposed a method and it has proven with utilizes any clustering algorithm to detect the drifting the help of results of node importance, which is related concepts. Our-NIR method is modified by help of to calculate the node importance that is very useful in probability distribution so that the name this method clustering of categorical data, still it has deficiency that is referred as POur-NIR. We adopted sliding is importance of data labeling and outlier detection. In this paper we modified Our-NIR method for evaluating window technique and initial data (at time t=0) is of node importance by introducing the probability used in initial clustering. These clusters are distribution which will be better than by comparing the represented by using POur-NIR (Our-NIR with the results. probability), where each attribute value importance is measured. By this method we can find whether the Keywords- clustering, NIR,Our-NIR, Categorical data points in the next sliding window (current data and node. sliding window) belongs to appropriate clusters of last clustering results or they are outliers which is I. INTRODUCTION future direction. We call this clustering result as a temporal and compare with last clustering result to Extracting Knowledge from large amount of data drift the data points or not. If the concept drift is not is difficult which is known as data mining. Clustering detected to update the POur-NIR otherwise dump is a collection of similar objects from a given data set attribute value based on importance and then and objects in different collection are dissimilar. reclustering using clustering techniques. However we Most of the algorithms developed for numerical data are comparing the node importance values of various may be easy, but not in Categorical data [1, 2, 12, methods with POur-NIR this paper. 13]. It is challenging in categorical domain, where The rest of the paper is organized as follows. the distance between data points is not defined. It is In section 2 discussed related work, in section 3 basic also not easy to find out the class label of unknown notations and Node representation provided, in data point in categorical domain. Sampling section 4 POur-NIR method for node importance techniques improve the speed of clustering and we discussed and also contains results with comparison consider the data points that are not sampled to of Ming-Syan Chen method and Our-NIR method allocate into proper clusters. The data which depends and finally concluded with section 5. on time called time evolving data. For example, the buying preferences of customers may change with II. RELATED WORK time, depending on the current day of the week, availability of alternatives, discounting rate etc. Since In this section, we discuss various clustering data evolve with time, the underlying clusters may algorithms on categorical data with cluster 146 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 4, April 2011 representatives and data labeling. We studied many III. NODE REPRESENTATION data clustering algorithms with time evolving.Cluster representative is used to summarize and characterize For categorical or mixed data can have several the clustering result, which is not fully discussed in representations. But in our work we can take two categorical domain unlike numerical domain. sorts of data representations. In first kind of In K-modes which is an extension of K-means representation every data point present in the sliding algorithm in categorical domain a cluster is window or data slide is divided into distinct points in represented by ‘mode’ which is composed by the which every distinct point is considered as the new most frequent attribute value in each attribute domain node and each node has two parts, in this name or the in that cluster. Although this cluster representative is categorical value is placed in the pre-part of the node simple, only use one attribute value in each attribute where as the post-part contains the numerical value domain to represent a cluster is questionable. It of that data point or the node. For example: nodes composed of the attribute values with high co- with attribute name “COMPOSE” which is a occurrence. In the statistical categorical clustering categorical part and the number of occurrences in the algorithms [3,4] such as COOLCAT and LIMBO, document ‘24’ is a numerical part. This node is data points are grouped based on the statistics. In represented as follows: algorithm COOLCAT, data points are separated in Node [COMPOSE: 24] such a way that the expected entropy of the whole This representation eventually reduces the arrangements is minimized. In algorithm LIMBO, the ambiguity that may prevail among the attributes, as information bottleneck method is applied to minimize many attributes may have same value. By introducing the information lost which resulted from the categorical part into the node we eliminate the summarizing data points into clusters. However, all risk of confusion. There is another form of of the above categorical clustering algorithms focus representation of the data in our work. In this second on performing clustering on the entire dataset and do representation we use a data description file that not consider the time-evolving trends and also the describes the data attributes and with a transitive clustering representatives in these algorithms are not relation we recognize the data attribute. This is the clearly defined. simplification of the above mentioned representation the only difference is that categorical part is kept in In this paper, first object of our method which is another file. This may look like the numerical based on the idea of representing the clusters by the representation of the data at an instance, but the value importance of the attribute values. This that is used to represent an attribute may be a representation is more efficient than using the numerical, binary, or categorical. This eases the representative points. effort that is required. This representation is also After scanning the literature, it is clear that clustering useful for the importance of node in the data set used categorical data is un touched many ties due to the in our work. complexity involved in it. A time-evolving IV. IMPORTANCE OF NODE: POur‐NIR categorical data is to be clustered within the due course hence clustering data can be viewed as The distribution of Node that is described in follows: there are a series of categorical data points D above section represents a cluster. As mentioned is given, where each data point is a vector of q every node has attribute value, the same value is used attribute values, i.e., pj=(pj1,pj2,...,pjq). And A = to find the distribution of the data points. Hence the {A1,A2 ,..., Aq}, where Aa is the ath categorical importance of the node plays a great role in finding attribute, 1 ≤ a ≤ q. The window size N is to be given clusters the importance of the node is evaluated with so that the data set D is separated into several the following rules such as rule 1 , rule 2 and rule 3. continuous subsets St, where the number of data Here we considered a symbolic representation for the points in each St is N. The superscript number t is the ith node in cluster i is N [i, r], The number of data identification number of the sliding window and t is points in cluster Ci is mi , and k is number of clusters . also called time stamp. Here in we consider the first N data points of data set D this makes the first data Rule1 (Positive Probability of node N [i, r]): The slide or the first sliding window S0. Our intension is probability of node (pi )in the cluster can be to cluster every data slide and relate the clusters of calculated as follows: every data slide with previous clusters formed by the | N [i, r ] | previous data slides. Several notations and pi = ∑ k representations are used in our work to ease the z =1 | N [ z, r ] | process of presentation: 147 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 4, April 2011 Rule 2 (Negative Frequency of node N [i, r]): The provide in better manner for the clustering of data negative frequency of the node in the cluster is points on time based. calculated based on the probability of node N [i, r]. | N [ j, r ] | q j = 1− ∑ k z =1 | N [ z, r ] | Rule 3 (node distribution function): the node distribution function value can be calculated by the product of Rule 1 and Rule 2. d(N[r])= pI * πqj, j i Rule 4 (Weighted Function): the importance of node N [i, r]. can be calculated by the product of Rule 3 and frequency of node in ith cluster. Fig 1. Sample data points of categorical data and initially cluster N[i, r] performed for the sliding window 1. W ( ci, N [i, r])= * d(N [i, r]) mi Example 1: consider the data set in fig 1.cluster c11 contains three data points. The node {[A1=A]} occurs The weighting function is designed to measure the three times in c11and does not occurs in c12. distribution of the node between clusters based on the The importance of node {[A1=A]} in c11 and in c21 is information theorem [15]. calculated as follows the weight of the node The weighting function measures the entropy of d({A1=A}) = ((3/3)* (1-0/3)=1 and therefore an the node between clusters. Suppose that there is a importance of the node {A1=A} in cluster c11 is w(c1 node that occurs in all clusters uniformly. The node , {A1=A}) = (3/3)*1=1 and in cluster c21 , it is zero. that contains the maximum uncertainty provides less Similarly the remaining nodes as follows : Weight of clustering characteristics. Therefore, this node should the node d({A2=M})= ((3/6)*(1-3/6)=0.25 and have a small weight. Moreover, the maximum therefore node importance in cluster c11 is entropy value of a node between clusters equals. Our- w(c1,{A2=M})=(3/3)*0.25=0.25 , weight of the node NIR minimized the computation time because of d({A3=C})= ((2/2)*(1-0/2)=1 and node importance in normalization is not required where as in Chan cluster in cluster c11 is w (c1,{A3=C})=(2/3)*1=0.66, proposed method considered normalization and weight of the node d({A3=D})= ((1/1)*(1-0/1)=1 and highest frequency node getting the zero importance node importance in cluster in cluster c11 is w that give impurity clustering. Highest frequency node (c1,{A3=C})=(1/3)*1=0.33, similarly calculate the may get the relative importance by proposed POur- importance of the node values to the cluster c2, NIR Method which will reduce the impurity. The importance of the node N [i, r] in cluster ci is measured by multiplying the rule1 and rule 2 i.e., the weighting function W ( ci, N [i, r]). Note that the range of both the probability of N [i, r] being in ci and the weighting function W ( ci, N [i, r]) is [0, 1], implying that the range of the important value W ( ci, N [i, r]) is also in [0, 1]. Fig 2. Node importance values for cluster C1 and C2 The new method is related to the idea of conceptual clustering [9], which creates a conceptual structure to represent a concept (cluster) during clustering. However, NIR only analyzes the conceptual structure and does not perform clustering, i.e., there is no objective function such as category utility (CU) [11] in conceptual clustering to lead the clustering procedure. In this aspect our method can 148 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 4, April 2011 In this paper, we considered the previous work that is Our-NIR Method which is modified by the Probability distribution that is to find node importance of node. We analyzed by taking same example in this find the differences in the node importance values of attributes in same cluster which plays an important role in clustering. The future work deciding the class label of unclustered data point and therefore the result demonstrates that POur-NIR method is accurate as said in section 4, than by our previous Method and also it improves the performance of precision and recall of DCD. REFERENCES [1] C. Aggarwal, J. Han, J. Wang, and P. Yu, “A Framework for Clustering Evolving Data Streams,” Proc. 29th Int'l Conf.Very Large Data Bases (VLDB) ,2003. [2] C.C. Aggarwal, J.L. Wolf, P.S. Yu, C. Procopiuc, and J.S. Park, “Fast Algorithms for Projected Clustering,” Proc. ACM SIGMOD '99, pp. 61-72, 1999. [3] P. Andritsos, P. Tsaparas, R.J. Miller, and K.C. Sevcik, “Limbo: Scalable Clustering of Categorical Data,” Proc. Ninth Int'l Conf. Extending Database Fig 3 (a) NIR values of nodes for Cluster C1 Technology (EDBT), 2004. Fig 3 (b) NIR Values of nodes for Cluster C2 [4]D. Barbará, Y. Li, and J. Couto, “Coolcat: An Entropy-Based Algorithm for Categorical A. Comparison of Our‐NIR and POur‐NIR Clustering,” Proc. ACM Int'l Conf. Information and Knowledge Management (CIKM), 2002. Fig 2 shows the importance value of attributes with [5] F. Cao, M. Ester, W. Qian, and A. Zhou, size of window is 5. This study fixes for the two “Density-Based Clustering over an Evolving Data slides of data points with the time evolving that is Stream with Noise,” Proc. Sixth SIAM Int'l Conf. from t1 to t2. As we said the importance of the node Data Mining (SDM), 2006. in that way we comparing the node importance [6] D. Chakrabarti, R. Kumar, and A. Tomkins, values. The node importance values of our method is “Evolutionary Clustering,” Proc. ACM SIGKDD '06, provide in a different way that increase the purity of pp. 554-560, 2006. the cluster which is impact on the accuracy of [7] H.-L. Chen, K.-T. Chuang and M.-S. Chen, clustering. In figure 3 (a) and (b), we present the “Labeling Unclustered Categorical Data into Clusters importance values of the each system maintained and Based on the Important Attribute Values,” Proc. Fifth over the 4 attributes of each cluster with sliding IEEE Int'l Conf. Data Mining (ICDM), 2005. window size of given on importance of attributes of [8]H.-L. Chen, M.-S. Chen, and S-U Chen Lin POur-NIR method is showing in range 0 to 1 and it “Frame work for clustering Concept –Drifting has one major drawback that is if node occurs in both categorical data,” IEEE Transaction Knowledge and the clusters with highest frequency, even though it Data Engineering v21 no 5 , 2009. may get zero importance ,Our-NIR showing in the [9] D.H. Fisher, “Knowledge Acquisition via range 0.16 to 0.5 that means all the attributes might Incremental Conceptual Clustering,” Machine be getting more or less importance .POur-NIR Learning, 1987. Method showing the range 0.25 to 1 which is an [10]MM Gaber and PS Yu “Detection and average to the above methods so it can be better for Classification of Changes in Evolving Data Streams,” the maintenance of the purity clustering. International .Journal .Information Technology and Decision Making, v5 no 4, 2006. [11] M.A. Gluck and J.E. Corter, “Information V. CONCLUSION Uncertainty and the Utility of Categories,” Proc. Seventh Ann. Conf. Cognitive Science Soc., pp. 283-287, 1985. 149 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 4, April 2011 [12]G Hulton and Spencer,“Mining Time-Changing Data Streams” Proc. ACM SIGKDD, 2001. [13]AK Jain MN Murthy and P J Flyn “Data Clustering: A Review,” ACM Computing Survey, 1999. [14]O.Narsoui and C.Rojas,“Robust Clustering for Tracking Noisy Evolving Data Streams” SIAM Int. Conference Data Mining , 2006. [ 15]C.E. Shannon, “A Mathematical Theory of Communication,” Bell System Technical J., 1948. [16]S.Viswanadha Raju, H.Venkateswara Reddy andN.Sudhakar Reddy,” A Threshold for clustering Concept – Drifting Categorical Data”, IEEE Computer Society, ICMLC 2011. [17]S.Viswanadha Raju, H.Venkateswara Reddy and N.Sudhakar Reddy,” Our-NIR:Node Importance Representation of Clustering Categorical Data ”, International Journal of Computer Science and Technology (Accepted) 2011. 150 http://sites.google.com/site/ijcsis/ ISSN 1947-5500

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