Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Clustering data in an uncertain environment using an artificial immune system Presenter : Cheng-Hui Chen Author : A.J. Graaff *, A.P. Engelbrecht PRL 2011

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outlines Motivation Objectives Methodology Experiments Conclusions Comments

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation 3 Lymphocytes network Artificial immune system ， AIS Antigenic Antibody

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objectives  An artificial immune models applied to cluster and proposes clustering performance measures in a non-stationary environment.  The investigates different data migration types and proposes a technique to generate artificial non-stationary data. 4

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology 5 Lymphocyte Network Antigenic Compare three artificial immune models use to cluster non-stationary data  DWB  SMAIN  LNNAIS Artificial lymphocyte

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Data migration types ─ Pattern migration A feature vector can migrate from one cluster to another in the data set. ─ Cluster migration Instead of selecting a ratio of feature vectors for migration, a fraction of the number of clusters in the data set is selected for migration. ─ Centroid migration All the clusters in a data set adapt the spatial position of their centroids. 6

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Initialising B and determining A ─ DWB Bmax ─ LNNAIS Bmax( empty expands to Bmax over time) ─ SMAIN Binit 7  Calculating the affinity and stimulation level ─ DWB Euclidean distance and Initial radius of influence, ϕ min ─ LNNAIS Euclidean distance ─ SMAIN Euclidean distance  Determining the ALC networks ─ DWB Hybrid approach ─ LNNAIS Index based neighbourhood technique ─ SMAIN Network affinity threshold, NAT Proximity matrix  Adapting the ALCs in H ─ DWB If membership function value > threshold  Age is mature Mutation rate, ς ─ LNNAIS Mutation clone of the ALC in H ─ SMAIN If antigen affinity >NAT  Antigen is initialised as a cloned ALC  Determining the number of clusters ─ DWB K-means ─ LNNAI Index based neighbourhood technique ─ SMAIN Hierarchical agglomerative A: B:

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  The average compactness of the clusters.  The minimum separation between clusters is determined by inter min.  The validity index is based on the ratio of intra- clustering distance to the inter clustering distance. 8

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  The cluster quality, at time t  The mean value of measured clustering quality for a specific run, r  The collective mean quality 9

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Generating artificial non-stationary data 10

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 11

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Pattern migration 12

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Intelligent Database Systems Lab N.Y.U.S.T. I. M. 14 Cluster migration

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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Centroid migration 16

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Intelligent Database Systems Lab N.Y.U.S.T. I. M. 18  A non-parametric Mann– Whitney U test with a 0.95 confidence interval. ─ H0: There is no difference in Q. ─ H1: There is a difference in Q.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusions  The proposed clustering performance measures were used for comparison between three network based AIS models for clustering of the generated artificial non-stationary data sets. 19 DWBSMAINLNNAI Cluster of qualityLowHighMid CostHighMidLow

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments  Advantages ─ Context detailed  Drawback ─ …  Applications ─ Clustering in non-stationary environment. 20