Novel Pruning Based Hierarchical
Agglomerative Clustering For Mining Outliers
In Financial Time Series
Author(s)
D. Wang, P. J. Fortier & H. E. Michel
Abstract
Investors must make informed decisions using partial and imperfect information.
As accuracy and completeness of information held by the investor rise, the
probability for better decision making also rises. Similarity search based outlier
detection in financial time series is key to making better decisions for many
investment strategies and portfolio management techniques. This motivates
people to utilize numerous data mining techniques to discover similarities from
massive financial time series data pools. The research introduces a novel pruning
based Hierarchical Agglomerative Clustering (HAC) algorithm to search for
similarity among financial time series in high dimensional space using securities
in the S&P500 index as experimental data. The algorithm is based on vertical
and horizontal dimension reduction algorithms [11] and a unique similarity
measurement definition [12] with the time value concept. This paper discloses a
series of experiment results that illustrate the effectiveness of the algorithm.
Keywords: outlier, data mining, computational finance, financial time series,
similarity search, high dimension, clustering.
1 Introduction
We propose a novel similarity search in high dimensional financial time series
by using a pruning based HAC algorithm. The similarity search is performed
after dimensionality reduction, which composes of an Attributes Selection (AS)
algorithm [11] and a Piecewise Linear Representation (PLR) based Segmentation
Keywords
outlier, data mining, computational finance, financial time series,similarity search, high dimension, clustering.
Related Book
Other papers in this volume
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