Visualisation of Cluster Dynamics and Change Detection in Ubiquitous Data Stream Mining Authors Brett Gillick, Mohamed Medhat Gaber, Shonali Krishnaswamy, and Arkady Zaslavsky Caulfield School of Information Technology, Monash University, 900 Dandenong Rd, Caulfield East, Victoria, 3145, Australia

Introduction Power of handheld devices is increasing Ubiquitous Data Mining (UDM) allows “anytime, anywhere” analysis [4,5] UDM data mining algorithms have been developed [1,3] Visualisation is useful in traditional DM Apply visualisation in UDM to assist with, and speed up, the decision making process for mobile users

Related work Kargupta et al [3] have proposed “MobiMine” a system where the data mining is conducted on a central server. The results are compressed using Fourier transformation. The compressed results are sent to the mobile device for visualization

Change detection & cluster dynamics visualisation model As seen in [2] change detection algorithm must be trained Lightweight Clustering module Incoming data stream is clustered Change Detection module Periodically, this algorithm is run with current statistical information compared to stored information in order to detect changes Visualisation module Continuously updates a visualisation of clusters and any change detection information that has been generated

Visualisation of Cluster Dynamics Lightweight Clustering (LWC) algorithm [1] Threshold-based One pass clustering algorithm Produces cluster and weight information Visualise Cluster positions Cluster weights

Cluster dynamics visualisation algorithm 1. Let m be the number of history sets of clusters stored in memory 2. Let there be n = { CS 1, CS 2, …, CS m } sets of clusters resulting from the clustering algorithm where CS 1 is the current set of clusters and CS m is the oldest stored set of clusters 3. Let there be CC = { cc 1, cc 2, …, cc n } cluster centres in each CS 4. Let C = { c 1, c 2, …, c m } be a set of colour codes indicating the cluster set’s time stamp 5. A colour c i, is assigned to represent a particular cluster set CS j where i,j=1..m 6. Let G be the graphical object used in the visualization to represent a cluster centre and GW be the graphical object associated with G representing the cluster’s weight 7. Each CC will be coloured according to its cluster set with colour c i 8. The size of each cluster’s enclosing object will be equal to the cluster’s weight

Visualisation of Change Detection STREAM-DETECT algorithm (presented earlier) [2] Produces notifications of significant changes in Cluster domain Cluster distribution (uniform, normal) Visualise Sets of clusters before & after change

Change detection visualisation algorithm 1. Let CS 1 be the set of clusters before a detected change 2. Let CS 2 be the set of clusters after a detected change 3. Let c 1 be the colour used to indicate a pre-change set of clusters 4. Let c 2 be the colour used to indicate a post-change set of clusters 5. Let G be the graphical object used in the visualization to represent a cluster centre and GW be the graphical object associated with G representing the cluster’s weight 6. Each cluster in CS 1 will be assigned the colour c 1 7. Each cluster in CS 2 will be assigned the colour c 2

Implementation J2ME using the Connected Limited Device Configuration (CLDC) 1.1 and Mobile Information Device Profile (MIDP) 2.0 Mobile 3D Graphics (M3G) library which is an optional package for J2ME and runs alongside MIDP Emulators from the Mobility Pack for Netbeans 4.1 Data generator

Implementation Cluster positions taken from three numerical attributes Positions and weights of clusters are shown in the display Using transparency, sets of previous clusters are displayed in order to show cluster dynamics User is able to control camera to allow relative cluster positions to be examined

Implementation Neutral colour used for normal cluster information Active colour used to alert the user

Conclusion We have proposed our model for the visualisation of cluster dynamics and cluster change detection using our visualisation framework The visualisation module is able to display a 3D view of clusters Alerts are given to users about significant changes using an ‘active’ colour

References [1] Gaber, M. M., Krishnaswamy, S., Zaslavsky, A.: Cost-Efficient Mining Techniques for Data Streams, Australasian Workshop on Data Mining and Web Intelligence (DMWI2004), Dunedin, New Zealand (2004) [2]Gaber, M. M., Yu P. S.: Classification of Changes in Evolving Data Streams using Online Clustering Result Deviation, submitted to the 3rd International Workshop on Knowledge Discovery from Data Streams to be held in conjunction with ICML'06, June [3]Kargupta, H., Park, B., Pittie, S., Liu, L., Kushraj, D., Sarkar, K.: MobiMine: Monitoring the Stock Market from a PDA. ACM SIGKDD Explorations, Volume 3, Issue 2. ACM Press (2002) [4]Kargupta, H., Bhargava, R., Liu, K., Powers, M., Blair, P., Bushra, S., Dull, J., Sarkar, K., Klein, M., Vasa, M., Handy, D.: VEDAS: A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle Monitoring. Accepted for publication in the Proceedings of the SIAM International Data Mining Conference, Orlando. (2004) [5]Zaki, M. J.: Online, Interactive and Anytime Data Mining, guest editorial for special issue of SIGKDD Explorations, Volume 3, Issue 2 (2002) i-ii