Group05 Proposal

Visual Application for Time Series Clustering

Project Proposal

Poster

Final Report

Application

Abstract

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Background on Time Series Clustering

What is Time Series Clustering?

Clustering is a data analysis technique for organizing observed data (e.g. people, things, events, brands, companies) into meaningful taxonomies, groups or clusters without advanced knowledge of the groups’ definition. Clusters are formed based on combinations of input variable, which maximizes the similarity of cases within each cluster while maximizing the dissimilarity between groups that are initially unknows. A special type of clustering is time-series clustering, which is essentially dynamic data as its feature values changes as a function of time.

Key Parameters of Time-Series Clustering

Parameters	Algorithm
Type	Hierarchical Clustering Partitional Clustering
Distances	Dynamic Time Wrapping (DTW) Global Alignment Kernels (GAK) Shape-Based Distance (SBD)
Centroid	DTW Barycenter Averaging (DBA) Partitioning Around Medoids (PAM) Shape Averaging (Shape)

Packages Used

This dashboard mainly uses dtwclust package from R.

dtwclust:
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Objective

To build an application for Time Series Clustering

Time-series data are of interest due to their ubiquity in various areas ranging from science, engineering, business, economics, healthcare, to government. This dashboard aims to allow user to do time series clustering on time series related data to uncover patterns which have potential use case in the respective domain.

Reference

File:Time Series Clustering A Decade Review.pdf