Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Matching and indexing sequences of different lengths
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
ACM Computing Surveys (CSUR)
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Multivariate Clustering by Dynamics
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An Index-Based Approach for Similarity Search Supporting Time Warping in Large Sequence Databases
Proceedings of the 17th International Conference on Data Engineering
Haar Wavelets for Efficient Similarity Search of Time-Series: With and Without Time Warping
IEEE Transactions on Knowledge and Data Engineering
Adaptive dimension reduction for clustering high dimensional data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Incremental, Online, and Merge Mining of Partial Periodic Patterns in Time-Series Databases
IEEE Transactions on Knowledge and Data Engineering
Exact indexing of dynamic time warping
Knowledge and Information Systems
Clustering Time Series with Clipped Data
Machine Learning
FTW: fast similarity search under the time warping distance
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
New Time Series Data Representation ESAX for Financial Applications
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Online clustering of parallel data streams
Data & Knowledge Engineering
Characteristic-Based Clustering for Time Series Data
Data Mining and Knowledge Discovery
An Interweaved HMM/DTW Approach to Robust Time Series Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Clustering Multimedia Data Using Time Series
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01
Compression-based data mining of sequential data
Data Mining and Knowledge Discovery
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Optimal implementations of UPGMA and other common clustering algorithms
Information Processing Letters
Time series clustering and classification by the autoregressive metric
Computational Statistics & Data Analysis
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Representing financial time series based on data point importance
Engineering Applications of Artificial Intelligence
Toward accurate dynamic time warping in linear time and space
Intelligent Data Analysis
A generalized model for financial time series representation and prediction
Applied Intelligence
Inaccuracies of Shape Averaging Method Using Dynamic Time Warping for Time Series Data
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
A type-2 fuzzy rule-based expert system model for stock price analysis
Expert Systems with Applications: An International Journal
Proceedings of the VLDB Endowment
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Information Retrieval
Adapting the right measures for K-means clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering of discretely observed diffusion processes
Computational Statistics & Data Analysis
Research of SAX in Distance Measuring for Financial Time Series Data
ICISE '09 Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering
Clustering of time series data-a survey
Pattern Recognition
A novel two-level clustering method for time series data analysis
Expert Systems with Applications: An International Journal
Clustering Indian stock market data for portfolio management
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Time series labeling algorithms based on the K-nearest neighbors' frequencies
Expert Systems with Applications: An International Journal
Discovering clusters in motion time-series data
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Similarity search on time series based on threshold queries
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Empirical comparison of clustering methods for long time-series databases
AM'03 Proceedings of the Second international conference on Active Mining
A novel bit level time series representation with implication of similarity search and clustering
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Multimedia retrieval using time series representation and relevance feedback
ICADL'05 Proceedings of the 8th international conference on Asian Digital Libraries: implementing strategies and sharing experiences
Expert Systems with Applications: An International Journal
Experimental comparison of representation methods and distance measures for time series data
Data Mining and Knowledge Discovery
Expert Systems with Applications: An International Journal
A spatial contagion measure for financial time series
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
An automatic stock market categorization system would be invaluable to individual investors and financial experts, providing them with the opportunity to predict the stock price changes of a company with respect to other companies. In recent years, clustering all companies in the stock markets based on their similarities in the shape of the stock market has increasingly become a common scheme. However, existing approaches are impractical because the stock price data are high-dimensional data and the changes in the stock price usually occur with shift, which makes the categorization more complex. Moreover, no stock market categorization method that can cluster companies down to the sub-cluster level, which are very meaningful to end users, has been developed. Therefore, in this paper, a novel three-phase clustering model is proposed to categorize companies based on the similarity in the shape of their stock markets. First, low-resolution time series data are used to approximately categorize companies. Then, in the second phase, pre-clustered companies are split into some pure sub-clusters. Finally, sub-clusters are merged in the third phase. The accuracy of the proposed method is evaluated using various published data sets in different domains. We show that this approach has good performance in efficiency and effectiveness compared to existing conventional clustering algorithms.