Geo referenced time series is a cornerstone in spatiotemporal data analysis, revealing
invaluable patterns that drive socio-economic development. Previous studies have modeled …

Partial periodic pattern (3P) mining is a vital data mining technique that aims to discover all
interesting patterns that have exhibited partial periodic behavior in temporal databases …

Finding periodic-frequent patterns in temporal databases is a prominent data mining
problem with bountiful applications. It involves discovering all patterns in a database that …

Abstract Partial Periodic Pattern Mining (3PM) is a key knowledge discovery technique with
many applications. It involves discovering all patterns that have exhibited partial periodic …

High-dimensional datasets comprise a few rows but a huge total of features. The increased
difficulty data scientists have in efficiently gleaning insights from high-dimensional large data …

A geo-referenced time series is a crucial form of spatiotemporal data. Useful information that
can empower the users to achieve economic development is hidden in this series. When …

Partial periodic patterns are an important class of regularities in multiple time series data.
Most previous works focused on finding these patterns in a binary series by disregarding the …

An uncertain geo-referenced transactional database represents the probabilistic data
produced by stationary spatial objects observing a particular phenomenon over time. Useful …

Data mining is a collection of algorithms for extracting valuable insights from large amounts
of data. Data mining based approaches are being employed to improve the performance of …