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On Demand Classification of Data Streams. Charu C. Aggarwal Jiawei Han Philip S. Yu. Proc. 2004 Int. Conf. on Knowledge Discovery and Data Mining (KDD'04), Seattle, WA, Aug. 2004. Speaker: Pei-Min Chou Date:2005/04/01. Outline. Introduction Supervised Micro-cluster Snapshot
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On Demand Classification of Data Streams Charu C. Aggarwal Jiawei Han Philip S. Yu Proc. 2004 Int. Conf. on Knowledge Discovery and Data Mining (KDD'04), Seattle, WA, Aug. 2004 Speaker: Pei-Min Chou Date:2005/04/01
Outline • Introduction • Supervised Micro-cluster • Snapshot • Maintenance Supervised Micro-cluster • Training Data Stream • Classification on Demand • Empirical Results
Introduction • Advances in data storage often grow without limit referred to as data streams • one-pass mining model does not recognize the changes and it is too expensive to keep track of the entire history • static classification model likely to drop when there is a sudden burst • Our model simultaneous training and testing streams used for dynamic classification of data sets
Supervised Micro-cluster : Modify Micro-cluster • Only from training data and each with same class • Data streams • Multi-dimensional points with time stamps T1, … Tk …. • Each point contains d dimensions, i.e., • A micro-cluster for n points is defined as a (2*d + 4) tuple: • - the sum of the squares of the data values • - the sum of the data values • - the sum of the squares of the time stamps • - the sum of the time stamps • the number of data points • -variable corresponding to class id corresponds to • the class label of that micro-cluster
Snapshot • not too expensive to keep track history • storing the behavior of the micro-clusters at different moments in time • if (t mod 2i) = 0 but (t mod 2i+1)!= 0 • reaches max capacity, the oldest snapshot in this frame is removed • geometric time frame • vary from 0 to a value no larger than log2(T), T is the maximum length of the stream • maximum number =(max capacity)*log2(T)
Maintenance Supervised Micro-clusters • Nearest neighbor and k-means algorithms • The initial micro-clusters is offline process offline ---answers various user queries based on the stored summary statistics • When a new data point Xik arrives, it is either added to a micro-cluster, or a new micro-cluster is created
Classification on Demand • Construct • Find the correct time-horizon • The value of kfit • Large or small horizon be chosen • Test
Find the correct time-horizon • Macro-clusters are created over a user-specified time horizon h • LetS(tc): the snapshot of micro-clusters at time tc S(tc-h): the snapshot of micro-clusters at time tc-h • The new set of micro-clusters N(tc-h) are created by subtractingS(tc-h) from S(tc) • Subtractive property • Let C1 and C2 be two sets of points such that Then
Training Data Stream • A small portion of the stream is used for the process of horizon fitting stream segment • kfit:the number of points in the data used and the value small as 1% of the data • remaining portion of the training stream is used for the creation and maintenance of the class-specific micro-clusters
The value of kfit • Horizon determined classification accuracy • Process executed periodically for changes • kfit should be small enough so that the points in it reflect the immediate locality of tc • Qfit :pre-specified number of time units • a part of the training stream • the class labels are known a-priori • Nearest neighbor procedure (XεQfit) • Find the closest micro-cluster in N(tc,h) to X • compare the class label and true label
Large or small horizon be chosen • The accuracy of all the time horizons which are tracked by the geometric time frame are determined • The p time horizons which provide the greatest dynamic classification accuracy by • First sight ---smallest • Stable stream ---large
Test • test stream is a separate process which is executed continuously throughout the algorithm • Insert Xt , nearest neighbor classication process is applied using each (Xt belong H) • results in the determination class lable • these p class labels reported as the relevant class
Empirical Results • Pentium III,512MB,WinXP • Both real and synthetic • Advantage • much higher classification accuracy • Good scalability in terms of dimensionality and the number of class labels • stable processing rate • Space-efficient