Download Improved Hierarchical Clustering Using Time Series Data

International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 1, January 2013)
Improved Hierarchical Clustering Using Time Series Data
V. Kavitha1, Dr. M. Punithavalli2
1
2
Research Scholar, Karpagam University, Coimbatore, India
Director, Department of Master of Computer Applications, Sri Ramakrishna Engineering College, Coimbatore, India
These algorithms can able to process each example in
execution time and cluster qualities, while constantly
contribute a compact data description at each given
moment. Generally Clustering methods can handle
different dataset into five major categories:
Partitioning methods, Hierarchical methods, Density
Based methods, Grid Based methods and Model Based
methods.
The majority of the works in the clustering time series
data streams are based on incremental clustering rather than
the variable clustering. Incremental update with new data
point is the main characteristic of the new algorithm. And
the new algorithm can able to discover and respond to
changes that may occur in it.
Abstract— Mining Time series data has a remarkable
development of interest in today’s world. This paper presents
and evaluates an incremental clustering structure for time
series data stream. The new algorithm is called Improved
Hierarchical Clustering Algorithm (IHCA) is developed and
applied with ECG data set. This system continuously
constructs a tree structure of hierarchy that progress with
data set. Two kinds of operations need to grow the
Hierarchical clustering algorithm. The operations are split
and merge (reaggregate). According to the diameter of the
cluster the specific operation is decided. The split operation is
based on dissimilarity measure between time series data
points. The merge operation is to combine a previous split
node in order to reacts the changes in the correlation
structure between time series data points. These two operators
are adopting the fast arrival of time series data flow. Cluster
quality, Outlier and compilation time are the main features of
this research. Experimental results shows that the
performance of cluster quality and computation speed are
improved.
II. RELATED WORK
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A clustering is a group of data objects that are similar to
one another within the same cluster and are dissimilar to
the objects in other clusters. Clustering is the assignment of
a set of observations into subsets. However, clustering is a
difficult problem combinatorial, and differences in
assumptions and contexts in different communities have
made the transfer of useful generic concepts and
methodologies slow to occur. This research is belongs to
hierarchical clustering. Hierarchical clustering is one of the
incremental clustering. A good clustering method produces
high-quality clusters to ensure that the inter-cluster
similarity is low and the intra-cluster similarity is high; in
other words, members of a cluster are more like each other
than they are like members of a different cluster.Basically
clustering is based on two categories. They are namely
descriptive clustering and predictive clustering.
This algorithm describes descriptive models, that is, the
unsupervised learning functions. These functions do not
expect a target value, but focus more on the essential
structure, relations, interconnectedness, etc of the data.
Keywords— Times Series Data Stream, Hierarchical
Clustering, Similarity Distance.
I. INTRODUCTION
Time Series Data has a wonderful growth of awareness
in today’s world. Clustering time series is a challenging
one when the data base is large. For this reason many
researchers are involved in time series clustering. A time
series is a progression of real numbers. Each number
represents a time point assessment. Time series data stream
applications require continues monitoring. Quicker
response is required for time series data stream. For
example, the sequence could represent stock or commodity
prices, sales, exchange rates, weather data, biomedical
measurements etc. Recently, stream time series data
management has become a hot research topic due to its
wide application usage.
A data stream is an structured sequence of points x1, , , ,
, , , xn that must be accessed in order and that can be read
only once or a small number of time. The new high speed
data set will not adopt by the traditional algorithms. In this
way the innovative algorithms have been developed. These
algorithms should be able to purify the cluster structure
whenever more information is available and to take into
account that the structure can change over time.
A. Hierarchical Clustering
A hierarchical Clustering method generates a
hierarchical decomposition of the given set of data objects.
A hierarchical can be classified as being either
agglomerative or divisive.
569
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 1, January 2013)
The Agglomerative also called the bottom up approach
starts with each object forming a separate group. It
successively merges the objects or groups that are close to
one another, until all of the groups are merged into one
hierarchy. The divisive approach is also called the top down
approach, starts with the entire object in the same cluster.
For each iteration, a cluster is split up into smaller clusters,
until eventually each object is in one cluster, or until a
termination condition holds. Hierarchical does not require us to
pre specify the number of clusters and most hierarchical
algorithms are deterministic.
Advantage of Hierarchical Clustering
It means that it is a unsupervised process. Using this
algorithm the computation speed is decreased and the
cluster quality is improved. A good clustering method
produces high-quality clusters to ensure that the intercluster similarity is low and the intra-cluster similarity is
high.
A. Improved Hierarchical Clustering
In this paper, the Improved Hierarchical Clustering
algorithm [IHCA] is presented, which is an algorithm for
an incremental clustering of streaming time sequence. It
constructs a hierarchical tree-shaped structure of clusters by
using a top-down strategy. The leaves are the resulting
clusters, with each leaf grouping a set of variables. The
system includes an incremental distance measure and
executes procedures for expansion and aggregation of the
tree based structure. The system will be monitoring the
flow of continuous time series data. Then time interval will
be fixed. Within the specific time interval the data points
will be partitioned. In a partition the diameter is calculated.
Diameter is nothing but the maximum distance between the
two points. Each and every data point of the partition will
be compare with the diameter value. If the data point is
greater than the diameter value then the split process will
be execute otherwise the
Aggregate (Merge) process will be performed. Based on
the above criteria the hierarchical tree will be growing.
Here we have to observe the splitting process, because the
splitting will decide the growth of clusters. In the proposed
technique the Hoeffding Bound is used for to observe the
splitting process. In the proposed technique, IHCA the
technique unequality vapnik Chervonenkis is used for
splitting process. Using this technique the observation of
splitting process is improved. So, the cluster is grouping
properly.
In the Hoeffding Bound,
 Comes at the cost of lower efficiency.
 It has a logical structure, is easy to read and
interpret.
Disadvantage of Hierarchical Clustering
 Not able to find out the optimum centroid point
using Hierarchical Clustering.
 Unable to handle the large database due to the high
dimensionality problem.
 Distance between the particles within the cluster
will be high.
 Outlier (The data points are out of the range) is
more.
 Compilation Time is more. And Cluster Quality
(Intra Cluster and Inter Cluster) is less using
Hierarchical Clustering.
B. Clustering Streaming Time Series
Data streams usually consist of variables producing
examples continuously over time. Let X={xt1, xt2,….. xtn} be
the example containing the observations of all streams xi at
a specific time t. The goal of a clustering system for
multiple time series is to find a partition P of streams,
where streams in the same cluster tend to be more alike
than streams in different clusters.
One of the most widely used clustering approaches is
hierarchical clustering, due to the great visualization power
it offers [8, 11]. Hierarchical clustering produces a nested
hierarchy of groups of similar objects, according to a pair
wise distance matrix of the objects. One of the advantages
of this method is its generality; the user does not require
offering any parameters such as the number of instants.
However, its application is limited to small datasets, due to
its quadratic computational complexity.
(1)
Where, the observations starting that after n independent
observations of the real valued random variable r with
range R, with confidence
In the proposed algorithm, the range value will be
increase from R2 [1]to RN .[2] So the observation process is
not a fixed one. Depends on the number of nodes the
system will generating the observation process.
III. METHODOLOGY
IV. EXPERIMENTAL EVALUATION
In the proposed system the new hierarchical algorithm is
called Improved Hierarchical Clustering algorithm [IHCA]
is developed. This new algorithm is based on descriptive
clustering method.
Improved Hierarchical Clustering Algorithm is an
incremental system for time series data streams.
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International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 1, January 2013)
Since the scope of the system is very well defined, So
The system is applied to a set of time series with clustering
structure. However, if the streams present dynamic
performance, then the system should notice the changes in
the structure of the cluster and adapt it accordingly. We
must evaluate how the system performs on real data
produced by applications that generate time series data
streams.
Step 2: Mean of the Intra cluster is found out.
Step 3: All the data points of the clusters will be
comparing with the mean value.
Step 4: After comparison, each data point will be
decided whether the point will position within a cluster
or out of the cluster.
A. Evaluation Criteria for Clustering Quality
Generally, the criteria used to evaluate clustering
methods concentrate on the quality of the resulting clusters.
Given the hierarchical characteristics of the system, the
quality of the hierarchy is constructed by our algorithm.
And another evaluation criterion is computation time of the
system.
This proposed method is evaluated with different kinds
of time series data sets. Three types of data sets are used to
evaluate the proposed algorithm. The data sets are namely
ECG Data, EEG Data and Network Sensor Data. ECG
Data set is used to find out the anomaly Identification. This
data set have three attributes namely time seconds, left
peek and right peek. EEG Data set is used to find out
abnormal personality. The name of the attributes is Trial
number, Sensor value, Sensor position, Sample number.
The third type of data set is Network sensor. The name of
he attributes is Total bytes, in bytes, out bytes, Total
Package, in package, out package, Events.
V. SYSTEM EVALUATION ON TIME SERIES DATA SET
B. Cluster Quality
A good clustering algorithm will produce high quality
based on intra cluster similarity and inter cluster similarity
measures. The quality of the clustering result depends on
the similarity measure used by the method and its
implementation. The quality of a clustering method is also
measured by its ability to discover some or all of the hidden
patterns. The criteria for measuring the cluster quality of
intra clusters similarity will be high. And the inter cluster
similarity will be low. For analysing cluster quality will be
in two forms, First one is finding groups of objects will be
related to one another. And second one is finding the group
of objects that differ from the objects in other groups.
A. Record Set Specification
TABLE I
DATA SET SPECIFICATION
Data Set
ECG
EEG
Sensor Network
Number of Instance
1800
1644
2500
Number of Attributes
3
4
7
Using the above three kinds of data sets we have to
calculate Execution time of the system, Intra cluster , Inter
cluster and outlier of the cluster.
C. Computation Time
Another evaluation of this work is calculating the
computation time of the process. The complexity of
execution time will be decreased when using the proposed
work.
B. Result of Outlier
TABLE 2
OUTLIER SPECIFICATION
D. Outlier
Outlier is nothing but, the data points which are out of
the range of the cluster. The outlier is calculated for the
existing method of ODAC(Online Divisive Agglomerative
Clustering) and the proposed method IHCA (Improved
Hierarchical Clustering Algorithm).
Technique
Existing System(ODAC)
Proposed System(IHCA)
Outlier Points
152
123
C. Result of Execution Time
The following table shows that the difference between
the two techniques of the system execution time.
Outlier Calculation
Step 1: Intra Cluster value is calculated for all
Clusters.
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International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 1, January 2013)
TABLE 3
EXECUTION TIME BETWEEN EXISIING AND PROPOSED
Existing System Time
in seconds
Proposed System
Time in seconds
No of
Clusters
ODAC
IHCA
2
1.9066
1.8969
4
1.9216
1.8992
6
1.9766
1.8998
8
1.9838
1.9012
10
1.9259
1.9114
Figure 2 Intra cluster between exisiing and proposed system
TABLE 5
INTER CLUSTER BETWEEN EXISIING AND PROPOSED
SYSTEM
Existing System Inter
Cluster
Proposed System
Inter Cluster
ODAC
IHCA
2
330.64
375.84
4
227.72
279.07
6
198.27
215.34
8
121.67
148.74
10
101.27
119.89
No of
Clusters
Figure 1 Execution time between the existing and proposed systems
D. Result of Intra Cluster and Inter Cluster
TABLE 4
INTRA CLUSTER BETWEEN EXISIING AND PROPOSED
SYSTEM
Existing System Intra
Cluster
Proposed System Intra
Cluster
No of
Clusters
ODAC
IHCA
2
890.23
865.15
4
665.67
615.63
6
480.53
413.41
8
386.45
338.54
10
292.23
266.04
Figure 3 Inter cluster between exisiing and proposed system
The execution result proves that the Computation time,
Intra Cluster and Outlier performance is reduced than the
previous system. The Inter Cluster performance is
increased than the previous system.
572
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 1, January 2013)
REFERENCES
VI. CONCLUSION
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[10] Pedro P. Rodrigues ,‖ A Semi-Fuzzy Approach for Online DivisiveAgglomerative Clustering‖, is supported by a PhD grant awarded by
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[1]
Mining Time series data has a remarkable development
of interest in today’s world. This paper presents and
evaluates an incremental clustering structure for time series
data stream. The new algorithm is called Improved
Hierarchical Clustering Algorithm (IHCA) is developed
and applied with ECG data set.
This system continuously constructs a tree structure of
hierarchy that progress with data set. Split and Merge are
two operators adopting the fast arrival of time series data
flow. Cluster quality, Outlier and compilation time are the
main features of this research. Experimental results show
that the performances of cluster quality, Outlier and
computation speed are improved.
VII. FUTURE WORK
In this work, we have to face some of the problems. The
draw backs are
 Centroid Points are not optimized.
 Repeated data points are more.
 To reduce the outlier.
 To improve the Cluster Quality and Computation
Time.
The above problems are faced in this work. Using
innovative techniques to avoid the problems.
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