Negative Co-Location Pattern Mining Algorithms

Due to the widespread application of geographic information systems (GIS) and GPS technology and the increasingly mature infrastructure for data collection, sharing, and integration, more and more research domains have gained access to high-quality geographic data and created new ways to incorporate spatial information and analysis in various studies. There is an urgent need for effective and efficient methods to extract unknown and unexpected information, e.g., co-location patterns, from spatial datasets of high dimensionality and complexity. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial data by measuring the cohesion of a pattern. We present a model to measure the cohesion in an attempt to improve the efficiency of existing methods. The usefulness of our method is demonstrated by applying them on the publicly available spatial data of the city of Antwerp in Belgium. The experimental results show that our method is more efficient than existing methods.

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Spatiotemporal Pattern Mining: Algorithms and Applications

Frequent Pattern Mining ◽

10.1007/978-3-319-07821-2_12 ◽

2014 ◽

pp. 283-306 ◽

Cited By ~ 5

Author(s):

Zhenhui Li

Keyword(s):

Pattern Mining ◽

Spatiotemporal Pattern ◽

Mining Algorithms ◽

Spatiotemporal Pattern Mining

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Clustering of Time Series Data

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch042 ◽

2011 ◽

pp. 258-263

Author(s):

Anne Denton

Keyword(s):

Data Mining ◽

Time Series ◽

Pattern Mining ◽

Time Series Data ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

Series Data ◽

Science And Engineering ◽

Data Mining Algorithms ◽

Mining Algorithms

Time series data is of interest to most science and engineering disciplines and analysis techniques have been developed for hundreds of years. There have, however, in recent years been new developments in data mining techniques, such as frequent pattern mining, that take a different perspective of data. Traditional techniques were not meant for such pattern-oriented approaches. There is, as a result, a significant need for research that extends traditional time-series analysis, in particular clustering, to the requirements of the new data mining algorithms.

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Data-Performance Characterization of Frequent Pattern Mining Algorithms

International Journal of Data Mining & Knowledge Management Process ◽

10.5121/ijdkp.2015.5105 ◽

2015 ◽

Vol 5 (1) ◽

pp. 51-70

Author(s):

Sayaka Akioka

Keyword(s):

Pattern Mining ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

Performance Characterization ◽

Mining Algorithms

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Optimization of Evolutionary Algorithm Using Machine Learning Techniques for Pattern Mining in Transactional Database

Handbook of Research on Applications and Implementations of Machine Learning Techniques - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-9902-9.ch010 ◽

2020 ◽

pp. 173-200

Author(s):

Logeswaran K. ◽

Suresh P. ◽

Savitha S. ◽

Prasanna Kumar K. R.

Keyword(s):

Evolutionary Algorithm ◽

Pattern Mining ◽

Fitness Function ◽

Search Space ◽

Machine Learning Techniques ◽

Dynamic Selection ◽

Learning Techniques ◽

Optimal Function ◽

High Utility ◽

Mining Algorithms

In recent years, the data analysts are facing many challenges in high utility itemset (HUI) mining from given transactional database using existing traditional techniques. The challenges in utility mining algorithms are exponentially growing search space and the minimum utility threshold appropriate to the given database. To overcome these challenges, evolutionary algorithm-based techniques can be used to mine the HUI from transactional database. However, testing each of the supporting functions in the optimization problem is very inefficient and it increases the time complexity of the algorithm. To overcome this drawback, reinforcement learning-based approach is proposed for improving the efficiency of the algorithm, and the most appropriate fitness function for evaluation can be selected automatically during execution of an algorithm. Furthermore, during the optimization process when distinct functions are skillful, dynamic selection of current optimal function is done.

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