Robust regression and outlier detection
Robust regression and outlier detection
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Using approximations to scale exploratory data analysis in datacubes
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
i3: intelligent, interactive investigation of OLAP data cubes
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Cubegrades: Generalizing Association Rules
Data Mining and Knowledge Discovery
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Entropy Based Approximate Querying and Exploration of Datacubes
SSDBM '01 Proceedings of the 13th International Conference on Scientific and Statistical Database Management
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A Unified Framework for Monitoring Data Streams in Real Time
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
FTW: fast similarity search under the time warping distance
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Subsequence matching on structured time series data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Scaling and time warping in time series querying
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Mining compressed frequent-pattern sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
CURE for cubes: cubing using a ROLAP engine
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
High-dimensional OLAP: a minimal cubing approach
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Mining Top-n Local Outliers in Constrained Spatial Networks
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Detecting Abnormal Trend Evolution over Multiple Data Streams
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
GAMPS: compressing multi sensor data by grouping and amplitude scaling
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Mining convergent and divergent sequences in multidimensional data
International Journal of Business Intelligence and Data Mining
Event cube: another perspective on business processes
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
An adaptive algorithm for online time series segmentation with error bound guarantee
Proceedings of the 15th International Conference on Extending Database Technology
Review spam detection via temporal pattern discovery
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
SHARD: a framework for sequential, hierarchical anomaly ranking and detection
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Monitoring and diagnosing indicators for business analytics
CASCON '13 Proceedings of the 2013 Conference of the Center for Advanced Studies on Collaborative Research
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Market analysis is a representative data analysis process with many applications. In such an analysis, critical numerical measures, such as profit and sales, fluctuate over time and form time-series data. Moreover, the time series data correspond to market segments, which are described by a set of attributes, such as age, gender, education, income level, and product-category, that form a multi-dimensional structure. To better understand market dynamics and predict future trends, it is crucial to study the dynamics of time-series in multi-dimensional market segments. This is a topic that has been largely ignored in time series and data cube research. In this study, we examine the issues of anomaly detection in multi-dimensional time-series data. We propose time-series data cube to capture the multi-dimensional space formed by the attribute structure. This facilitates the detection of anomalies based on expected values derived from higher level, "more general" time-series. Anomaly detection in a time-series data cube poses computational challenges, especially for high-dimensional, large data sets. To this end, we also propose an efficient search algorithm to iteratively select subspaces in the original high-dimensional space and detect anomalies within each one. Our experiments with both synthetic and real-world data demonstrate the effectiveness and efficiency of the proposed solution.