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Graph preprocessing
 Introduction
 Noise removal and data enhancement problem
 Noise removal and data enhancement on binary data
 Noise removal and data enhancement on graph data
 Noise removal and data enhancement tools
 Current research problems
 Future directions
Data cleaning techniques at the data analysis
stage
 Distance-based
 Local Outlier Factor (LOF) based approaches
 Clustering-based
 HCleaner, is a hyper clique-based data cleaner
Clustering Based Techniques
• Key assumption: normal data records belong to large and dense clusters, while
anomalies belong do not belong to any of the clusters or form very small clusters
• Categorization according to labels
o Semi-supervised – cluster normal data to create modes of normal behaviour. If a
new instance does not belong to any of the clusters or it is not close to any cluster,
is anomaly
o Unsupervised – post-processing is needed after a clustering step to determine the
size of the clusters and the distance from the clusters is required from the point to
be anomaly
• Anomalies detected using clustering based methods can be:
o Data records that do not fit into any cluster (residuals from clustering)
o Small clusters
o Low density clusters or local anomalies (far from other points within the same
cluster)
Clustering based outlier detection method for
noise removal
Clustering algorithms can detect outliers as a by
product of the clustering process
o Small clusters, which are far away from
other major clusters can be outliers
o This method is sensitive to the choice of
clustering algorithms
o it has difficulties in deciding which clusters
should be classified as outliers
Simple Example
Y
• N1 and N2 are regions of
normal behavior
• Points o1 and o2 are
anomalies
• Points in region O3 are
anomalies
N1
o1
O3
o2
N2
X
Clustering based outlier detection method for
noise removal
 Calculate a centroid of each cluster
o Noise objects are the ones that are farthest from
their corresponding cluster centroids
o Data is clustered using a K-means algorithm
available in the CLUTO clustering package
o Cosine similarity (distance) of each object from its
corresponding cluster centroid is recorded
o top E% objects obtained after sorting these objects
in ascending (descending) order with respect to this
similarity (distance)
o This constitute the noise objects in the data
Clustering based outlier detection method for
noise removal
 complexity of the algorithm is the same as that of an execution
of K-means O(kn)
o where k is the number of clusters and n is the number of
points
 If there is only one cluster, then the cluster based approach
becomes very similar to the distance based approach
 If every object is a separate cluster, then the cluster based
approach degenerates to the process of randomly selecting
objects as outliers
 Performs well only when the number of clusters is close to the
‘actual’ number of clusters (classes) in the data set
Clustering Based Techniques
• Advantages:
 No need to be supervised
 Easily adaptable to on-line / incremental mode suitable for
anomaly detection from temporal data
• Drawbacks
 Computationally expensive
o Using indexing structures (k-d tree, R* tree) may alleviate this
problem
 If normal points do not create any clusters the techniques may fail
 In high dimensional spaces, data is sparse and distances between
any two data records may become quite similar.
o Clustering algorithms may not give any meaningful clusters
Data cleaning techniques at the data analysis
stage
 Distance-based
 Local Outlier Factor (LOF) based approaches
 Clustering-based
 HCleaner, is a hyper clique-based data cleaner
The H-confidence Measure
• The h-confidence of a pattern P = {i1, i2,…, im}
• Illustration:
• A pattern P is a hyperclique pattern if hconf(P)>=hc, where hc is a
user specified minimum h-confidence threshold
Alternate Equivalent Definitions of hconfidence
 Given a pattern P = {i1, i2,…, im}
• Definition:
hconf ( P)  min{conf ({x}  {P  {x}}) | x {i1 , i2 ,..., im}}
• Definition:
hconf ( P)  min{conf ( X  Y ) | X , Y  {i1 , i2 ,..., im}& X  Y  P}
All-Confidence Measure
Omiecinski – TKDE 2003
Properties of Hyperclique Pattern
Anti-monotone
if P '  P, then hconf ( P ')  hconf ( P)
High Affinity Property
• High h-confidence implies tight coupling amongst all items in the pattern
Magnitude of relationship consistent with many Other measures
 Jaccard, Correlation, Cosine
Cross support property
• Eliminates patterns involving items that have very different support levels
Cross Support Property of h-confidence
 At high support, all patterns that involve
low support items are eliminated
At low support, too many spurious patterns
are generated that involve one high support
item and one low support item
 Given a Pattern P = {i1, i2,…, im}
 For any two Itemsets
Support distribution of the pumsb dataset
X ,Y  P
X Y  P & X Y  
hconf(P)

supp{X}
supp{Y}
Hyper clique based data cleaner
 Idea is to eliminate data objects that are not tightly
connected to other data objects in the data set
 Every pair of objects within a pattern is guaranteed to
have cosine similarity above a certain level
 H-confidence measure has three important properties
o Anti-monotone property
o Cross-support property
o Strong affinity property
 HCleaner generally leads to better performance as
compared to the outlier based data cleaning alternatives
Impact of noise removal on clustering analysis
 Clustering performance is not affected by the
elimination of random objects
 Percentage of noise objects removed by LOF,
CCleaner, and HCleaner increases, the entropy
generally goes down
 Clustering performance improves as more and more
noise or weakly-relevant objects are removed
 HCleaner provides the best clustering results
compared to other noise removal techniques across all
experimental cases
Impact of noise removal on clustering analysis
 When the percentage of noise objects is lower than
30%
o HCleaner yields significantly better clustering
performance
o percentage of objects being removed is increased
o HCleaner tends to have better (higher) F-measure
values than other noise removal techniques for the
most experimental cases
 HCleaner tends to be the best or close to the best
technique for improving clustering performance for
binary data
Impact of noise removal on Association
analysis
 HCleaner provides the best association results
compared to other noise removal techniques when
the percentage of noise objects is above 25%
 HCleaner provides the best performance for all
ranges of noise percentages considered
 HCleaner can achieve better performance when a
large portion of noise has been removed
Conclusions
 Performance of Clustering is very sensitive to the
specified number of clusters
 Number of clusters is very small, then this approach
has performance similar to that of the distance based
approach
 Number of clusters is very large, then this approach
becomes similar to the random approach for
removing noise
 Best performance is obtained when size-3
hyperclique patterns are used as filters
Conclusions
 HCleaner tends to provide better clustering
performance and high quality associations than other
data cleaning alternatives for binary data
 Better noise removal results in better data analysis
 Framework for evaluating the effectiveness of noise
removal techniques for enhancing data analysis is
presented
 Better noise removal yields better data analysis
Conclusions
• Data preprocessing can enhance critical information in data
• Highly applicable in various application domains
• Nature of data enhancement and noise removal problem is
• dependent on the application domain.
• Need different approaches to solve a particular problem
formulation.
Directions for future work
 study was restricted to unsupervised data mining
techniques at the data analysis stage
 HCleaner, CCleaner, and the LOF based method
were each the best in different situations, it could be
useful to consider a voting scheme that combines
these three techniques
 Investigate the impact of these noise removal
techniques on classification performance
 Same analysis can be done on different data sets like
graph dsata set
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