Partial periodic patterns play a significant role in identifying regularities within temporal databases. However, most existing research has focused on discovering these patterns in binary datasets, overlooking the critical insights about the quantitative values associated with the items. This study utilizes the principles of fuzzy sets to propose a novel model for discovering Fuzzy Partial Periodic Frequent Patterns (FPPFPs) within a quantitative temporal database. A robust depth-first search algorithm has also been proposed to uncover all FPPFPs. The proposed algorithm incorporates a novel pruning strategy that effectively reduces the search space and the computational cost required for discovering the FPPFPs. The experimental findings on synthetic and real-world datasets demonstrate the efficiency of the proposed algorithm. Finally, a case study utilizing air pollution data is provided to showcase the practical applicability and significance of the identified patterns.

Crucial information that can empower users with competitive information to achieve socio-economic development lies hidden in big data. Pattern mining aims to discover this needy information by finding user interest-based patterns in big data. Unfortunately, existing pattern mining libraries are limited to finding a few types of patterns in transactional and sequence databases. This paper tackles this problem by providing a cross-platform open-source Python library called PAttern MIning (PAMI). PAMI provides several algorithms to discover different types of patterns hidden in various types of databases across multiple computing architectures. PAMI also contains algorithms to generate various types of synthetic databases. PAMI offers a command line interface, Jupyter Notebook support, and easy maintenance through the Python Package Index. Furthermore, the source code is available under the GNU General Public License, version 3. Finally, PAMI offers several resources, such as a user's guide, a developer's guide, datasets, and a bug report.

A geo-referenced time series database represents the data generated by a set of fixed locations (or items) observing a particular phenomenon over time. Useful information that can facilitate the users to achieve socio-economic development lies hidden in this data. This paper introduces a novel model of Fuzzy Geo-referenced Periodic-Frequent Patterns (FGPFPs) that may exist in these databases. An FGPFP represents a set of frequently occurring neighboring items observed at regular intervals in a database. For example, an FGPFP in a traffic congestion database represents a set of neighboring road segments where people have regularly faced congestion problems. A novel pruning technique has been presented to effectively reduce the search space and the computational cost of finding the desired patterns. We have also proposed an efficient depth-first search algorithm to find all the desired patterns. Experimental results demonstrate that the proposed algorithm is efficient. Finally, we demonstrate our model’s usefulness by performing traffic congestion analytics.

Partial periodic pattern mining is an important model in data mining with many real-world applications. However, this model’s successful industrial application was hindered by the problem of combinatorial explosion of patterns, which involves generating too many patterns, most of which might be redundant or uninteresting to the user. Furthermore, the problem of combinatorial explosion increases the memory, runtime, and the energy requirements of a mining algorithm. This paper aims to tackle this challenging problem by proposing a novel model of maximal partial periodic pattern that may exist in a database. We also present a new tree structure and a pattern-growth algorithm, called Maximal Partial Periodic Pattern-growth (max3P-growth), to find all desired patterns effectively. Experimental results demonstrate that the proposed model prunes many redundant patterns, and the max3P-growth is efficient and scalable. Finally, we show the usefulness of our model with a case study on traffic congestion analytics.

Spatial High Utility Itemset Mining (SHUIM) is an important knowledge discovery technique with many real-world applications. It involves discovering all itemsets that satisfy the user-specified m inimum u tility (minUtil) i n a q uantitative spatiotemporal database. The popular adoption and the successful industrial application of this technique have been hindered by the following two limitations: (i) Since the rationale of SHUIM is to find all itemsets that satisfy the minUtil constraint, it often produces too many patterns, most of which may be redundant or uninteresting to the user. (ii) Specifying a right minUtil value is an open research problem in SHUIM. This paper tackles these two problems by proposing a novel model of top-k spatial high utility itemsets that may exist in a database. A new constraint, called dynamic minimum utility (dMinUtil), was explored to reduce the search space effectively. This constraint is based on a greedy search, where we raise its value through five thresholdraising strategies. An efficient single scan algorithm that employs depth-first search to find all top-k spatial high utility itemsets was also presented in this paper. Experimental results demonstrate that our algorithm is memory and runtime efficient. We will also demonstrate the usefulness of our algorithm with two real-world case studies.

Finding fuzzy frequent patterns in a quantitative database is a challenging problem of significant importance in many real-world applications. Past studies focused on mining these patterns in quantitative transactional databases by disregarding the spatiotemporal characteristics of an item in the database. This paper proposes a generic model of fuzzy frequent spatial pattern (FFSP) that may exist in a quantitative spatiotemporal database. Discovering FFSPs in a database is nontrivial and challenging due to its huge search space and high computational cost. A novel pruning technique, called neighborhood pruning, has been introduced to effectively reduce the search space and the computational cost of finding the desired itemsets. This technique facilitates the mining of FFSPs in large real-world databases practicable. We also present an efficient algorithm, called Fuzzy Frequent Spatial Pattern-Miner (FFSP-Miner), to find all desired patterns in the database. Experimental results demonstrate that FFSP-Miner is both memory and runtime efficient. Finally, we discuss the usefulness of our model with a case study on air pollution analytics.