Mining Interesting Patterns Research Articles

Efficient approach for mining high-utility patterns on incremental databases with dynamic profits

High-utility itemset mining (HUIM) is one of the heavily studied fields of data mining, which is due to its high compatibility with real-world applications. HUIM is a process of extracting a complete set of interesting patterns by considering the importance and the quantity of each item. Incremental high-utility itemset mining (IHUIM) further increases the compatibility of HUIM by mining from a data stream instead of a static database. It does this by mining interesting patterns in a single database scan and storing prior knowledge in a compact data structure. However, there is a critical limitation, because the importance of each item has to be guaranteed to remain unchanged throughout the data stream. This limitation hinders the compatibility of IHUIM to the real-world applications, because the importance of an item changes from time to time in the real world. Conventional IHUIM approaches consequently have to use outdated information when mining interesting patterns. This paper proposes a novel problem of mining high-utility itemsets in an incremental and dynamic profit environment in order to account for the fluctuation of the item's importance. Furthermore, a novel approach to mining patterns in this type of environment is introduced using an efficient list structure and tight upper bounds. Experiments on real and synthetic datasets show that the proposed approach performs well compared to the state-of-the-art approaches in terms of the runtime and memory usage, and it scales better than the other approaches.

Ontology-based semantic data interestingness using BERT models

The COVID-19 pandemic has generated massive data in the healthcare sector in recent years, encouraging researchers and scientists to uncover the underlying facts. Mining interesting patterns in the large COVID-19 corpora is very important and useful for the decision makers. This paper presents a novel approach for uncovering interesting insights in large datasets using ontologies and BERT models. The research proposes a framework for extracting semantically rich facts from data by incorporating domain knowledge into the data mining process through the use of ontologies. An improved Apriori algorithm is employed for mining semantic association rules, while the interestingness of the rules is evaluated using BERT models for semantic richness. The results of the proposed framework are compared with state-of-the-art methods and evaluated using a combination of domain expert evaluation and statistical significance testing. The study offers a promising solution for finding meaningful relationships and facts in large datasets, particularly in the healthcare sector.

Understanding citizens’ satisfaction with the government response during the COVID-19 pandemic in China: comprehensive analysis of the government hotline

PurposeThe objective of this study was to analyse the influencing factors of citizens' dissatisfaction with government services during the COVID-19 pandemic to help government departments identify problems in the service process and possible countermeasures.Design/methodology/approachThe authors first used cosine interesting pattern mining (CIPM) to analyse citizens' complaints in different periods of the pandemic. Second, the potential evaluation indices of customer satisfaction were extracted from the hotline business system through a hypothesis analysis and modelled using multiple regression analysis. During the index transformation and standardization process, a machine-learning algorithm of clustering and emotion analysis was adopted. Finally, the authors used the random forest algorithm to evaluate the importance of the indicators and obtain the indicators more important to citizen satisfaction.FindingsThe authors found that the complaint topic, appeal time, urgency of citizens' complaints, citizens' emotions, level of detail in the case record, and processing timeliness and efficiency significantly influenced citizens' satisfaction. When the government addresses complaints in a more standardized and efficient manner, citizens are more satisfied.Originality/valueDuring the pandemic, government departments should be more patient with citizens, increase the speed of the case circulation and shorten the processing period of appeals. Staff should record appeals in a more standardized manner, highlighting themes and prioritizing urgent cases to appease citizens and relieve their anxiety.

Retraction Note to: Mining interesting actionable patterns for web service composition

Retraction Note to: Mining interesting actionable patterns for web service composition

RETRACTED ARTICLE: Mining interesting actionable patterns for web service composition

Web services are the way of integrating the web related applications using the XML, SOAP, WSDL and UDDI protocols in a standardized manner. Web service composition is the primary task of composing variety of different services on composite applications. Effective web service composition is not an easy task. This paper proposes a novel framework for extracting the interesting actionable patterns for effective web services for composition. This algorithm utilizes utility based data mining for extracting high utility actionable patterns for web service composition. Experimental results show that HUI-Miner approach outperforms well in terms of running time efficiency for our framework.

A survey of pattern mining in dynamic graphs

AbstractGraph data is found in numerous domains such as for the analysis of social networks, sensor networks, bioinformatics, industrial systems, and chemistry. Analyzing graphs to identify useful and interesting patterns is an important research area. It helps understanding graphs, and hence support decision making. Since two decades, many graph mining algorithms have been proposed to identify patterns such as frequent subgraphs, paths, cliques, and trees. But most of them assume that graphs are static. This simplifying assumption makes it easy to design algorithms but discard information about how graphs evolve. This article provides a detailed survey of techniques for mining interesting patterns in dynamic graphs, which can serve both as an introduction and as a guide to recent advances and opportunities in this research area. The main tasks related to mining patterns in dynamic graphs are reviewed such as discovering frequent subgraphs, evolution rules, motifs, subgraph sequences, recurrent and triggering patterns, and trend sequences. In addition, an overview of strategies and approaches to solve dynamic graph mining problems is presented, and their advantages and limitations are highlighted. Various extensions are also discussed such as to discover patterns in data streams and big data. Finally, the article mentions several research opportunities.This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Algorithmic Development > Association Rules

Discovering subjectively interesting multigraph patterns

Over the past decade, network analysis has attracted substantial interest because of its potential to solve many real-world problems. This paper lays the conceptual foundation for an application in aviation, through focusing on the discovery of patterns in multigraphs (graphs in which multiple edges can be present between vertices). Our main contributions are twofold. Firstly, we propose a novel subjective interestingness measure for patterns in both undirected and directed multigraphs. Though this proposal is inspired by our previous related research for simple graphs (having only single edges), the properties of multigraphs make this transition challenging. Secondly, we propose a greedy algorithm for subjectively interesting pattern mining, and demonstrate its efficacy through several experiments on synthetic and real-world examples. We conclude with a case study in aviation, which demonstrates how the departure from an analyst’s prior beliefs captured as subjectively interesting patterns could help improve an analyst’s understanding of the data and problem at hand.

Educational data mining for students' performance based on fuzzy C‐means clustering

Education greatly aids in the process of students' growth; therefore, education institutions try to provide high-quality education to their students. A possible remedy to provide high-quality education is by discovering knowledge from educational data. However, accurately evaluating students' performance is very challenging due to different sources and structures of educational data. In addition, different teaching strategies are required because students' learning ability are different. One way to discover the hidden knowledge from educational data is the use of clustering algorithms, which are capable of mining interesting patterns from educational data. Thus, this study presents a fuzzy C-means clustering algorithm using 2D and 3D clustering to evaluate students' performance based on their examination results (the examination grades from College of Computer Science and Technology, Huaqiao University for students enrolled in year 2014). Based on the experimental results from 2D and 3D clustering for evaluating students' performance, the educators can better understand the students' performance so as to build a pedagogical basis for decisions. Students can also receive some recommendations from the mining results about their performance.

A Study of Pattern Prediction in the Monitoring Data of Earthen Ruins with the Internet of Things.

An understanding of the changes of the rammed earth temperature of earthen ruins is important for protection of such ruins. To predict the rammed earth temperature pattern using the air temperature pattern of the monitoring data of earthen ruins, a pattern prediction method based on interesting pattern mining and correlation, called PPER, is proposed in this paper. PPER first finds the interesting patterns in the air temperature sequence and the rammed earth temperature sequence. To reduce the processing time, two pruning rules and a new data structure based on an R-tree are also proposed. Correlation rules between the air temperature patterns and the rammed earth temperature patterns are then mined. The correlation rules are merged into predictive rules for the rammed earth temperature pattern. Experiments were conducted to show the accuracy of the presented method and the power of the pruning rules. Moreover, the Ming Dynasty Great Wall dataset was used to examine the algorithm, and six predictive rules from the air temperature to rammed earth temperature based on the interesting patterns were obtained, with the average hit rate reaching 89.8%. The PPER and predictive rules will be useful for rammed earth temperature prediction in protection of earthen ruins.

Interesting association rule mining with consistent and inconsistent rule detection from big sales data in distributed environment

Nowadays, there is an increasing demand in mining interesting patterns from the big data. The process of analyzing such a huge amount of data is really computationally complex task when using traditional methods. The overall purpose of this paper is in twofold. First, this paper presents a novel approach to identify consistent and inconsistent association rules from sales data located in distributed environment. Secondly, the paper also overcomes the main memory bottleneck and computing time overhead of single computing system by applying computations to multi node cluster. The proposed method initially extracts frequent itemsets for each zone using existing distributed frequent pattern mining algorithms. The paper also compares the time efficiency of Mapreduce based frequent pattern mining algorithm with Count Distribution Algorithm (CDA) and Fast Distributed Mining (FDM) algorithms. The association generated from frequent itemsets are too large that it becomes complex to analyze it. Thus, Mapreduce based consistent and inconsistent rule detection (MR-CIRD) algorithm is proposed to detect the consistent and inconsistent rules from big data and provide useful and actionable knowledge to the domain experts. These pruned interesting rules also give useful knowledge for better marketing strategy as well. The extracted consistent and inconsistent rules are evaluated and compared based on different interestingness measures presented together with experimental results that lead to the final conclusions.

TWINCLE : A Constrained Sequential Rule Mining Algorithm for Event Logs

Discovering workflow patterns in event-logs is important for many organizations to understand and optimize organizational processes. Although numerous algorithms have been proposed in the literature to discover patterns in sequences of symbols, most of them are inadequate to discover patterns in rich event-log data. In this paper, motivated by the analysis of patient pathways in the health domain, a rich type of event logs, called activity-cost event logs, is considered where each event is associated with a cost. The paper formalizes the problem of mining interesting low-cost patterns in these logs by combining novel concepts of penalties (activity costs) and consistency of patterns, with traditional measures of confidence, length, and time. Furthermore, to extract these patterns efficiently from event logs, an algorithm named TWINCLE (Time-WINdow, Cost and LEngth constrained sequential rule mining) is proposed. Experiments carried out on benchmark datasets and real-life healthcare event logs show that proposed algorithm is efficient and can discover interesting patterns.

Analysis of Protein Microarray Data Using Data Mining

Latest progress in biology, medical science, bioinformatics, and biotechnology has become important and tremendous amounts of biodata that demands in-depth analysis. On the other hand, recent progress in data mining research has led to the development of numerous efficient and scalable methods for mining interesting patterns in large databases. This paper bridge the two fields, data mining and bioinformatics for successful mining of biological data. Microarrays constitute a new platform which allows the discovery and characterization of proteins.

Pushing Constraints to Generate Top-K Closed Sequential Graph Patterns

In this paper, the problem of finding sequential patterns from graph databases is investigated. Two serious issues dealt in this paper are efficiency and effectiveness of mining algorithm. A huge volume of sequential patterns has been generated out of which most of them are uninteresting. The users have to go through a large number of patterns to find interesting results. In order to improve the efficiency and effectiveness of the mining process, constraints are more essential. Constraint-based mining is used in many fields of data mining such as frequent pattern mining, sequential pattern mining, and subgraph mining. A novel algorithm called CSGP (Constraint-based Sequential Graph Pattern mining) is proposed for mining interesting sequential patterns from graph databases. CSGP algorithm is revised to mine topk closed patterns and named as TCSGP (Top-k Closed constraint-based Sequential Graph Pattern mining). General Terms Data mining, Graph mining, Constraint-based mining.

Mining interesting patterns from uncertain databases

Due to a growing demand for efficient algorithms for mining frequent itemsets from uncertain databases, several approaches have been proposed in recent years, but all of them use support-based constraints to prune the combinatorial search space. Most real life databases contain data whose correctness is uncertain. The support-based constraint alone is not enough, because the frequent itemsets may have weak affinity. Even a very high minimum support is not effective for finding correlated patterns with increased weight or support affinity. There are a few approaches in precise databases that propose new measures to mine correlated patterns, but they are not applicable in uncertain databases because certain and uncertain databases differ both semantically and computationally. In this paper, we propose a new strategy: Weighted Uncertain Interesting Pattern Mining (WUIPM), in which a tree structure (WUIP-tree) and several new measures (e.g., uConf, wUConf) are suggested to mine correlated patterns from uncertain databases. To our knowledge, ours is the first work specifically to consider weight or importance of an individual item alongside correlation between items of patterns in uncertain databases. Additionally, we propose a new metric, prefix proxy value, pProxy for our WUIP-tree that helps improve the mining performance. A comprehensive performance study shows that our strategy (a) generates fewer but valuable patterns and (b) is faster than existing approaches even when affinity measures are not applied.

P-N-RMiner: a generic framework for mining interesting structured relational patterns

Methods for local pattern mining are fragmented along two dimensions: the pattern syntax, and the data types on which they are applicable. Pattern syntaxes include subgroups, n-sets, itemsets, and many more; common data types include binary, categorical, and real-valued. Recent research on relational pattern mining has shown how the aforementioned pattern syntaxes can be unified in a single framework. However, a unified model to deal with various data types is lacking, certainly for more complexly structured types such as real numbers, time of day—which is circular—, geographical location, terms from a taxonomy, etc. We introduce P-N-RMiner, a generic tool for mining interesting local patterns in (relational) data with structured attributes. We show how to handle the attribute structures in a generic manner, by modelling them as partial orders. We also derive an information-theoretic subjective interestingness measure for such patterns and present an algorithm to efficiently enumerate the patterns. We find that (1) P-N-RMiner finds patterns that are substantially more informative, (2) the new interestingness measure cannot be approximated using existing methods, and (3) we can leverage the partial orders to speed up enumeration.

Knowledge Discovery from Social Graph Data

High volumes of a wide variety of valuable data can be easily collected and generated from a broad range of data sources of different veracities at a high velocity. In the current era of big data, many traditional data management and analytic approaches may not be suitable for handling the big data due to their well-known 5V's characteristics. Over the past few years, several systems and applications have developed to use cluster, cloud or grid computing to manage and analyze big data so as to support data science (e.g., knowledge discovery and data mining). In this paper, we present a knowledge-based system for social network analysis so as to support big data mining of interesting patterns from big social networks that are represented as graphs.

Toward Quantifying Vertex Similarity in Networks

AbstractVertex similarity is a major concept in network science with a wide range of applications. In this work we provide novel perspectives on finding (dis)similar vertices within a network and across two networks with the same number of vertices (graph matching). With respect to the former problem, we propose to optimize a geometric objective that allows us to express each vertex uniquely as a convex combination of a few extreme types of vertices. Our method has the important advantage of supporting efficiently several types of queries such as, which other vertices are most similar to this vertex? by using appropriate data structures and by mining interesting patterns in the network. With respect to the latter problem (graph matching) we propose the generalized condition number—a quantity widely used in numerical analysis— κ(LG, LH) of the Laplacian matrix representations of G, H as a measure of graph similarity, where G, H are the graphs of interest. We show that this objective has a solid theoretical...

Tree-Based Weighted Interesting Pattern Mining Approach for Human Interaction Pattern Discovery

Human Interaction is an essential aspect to recognize communicative data in various applications including medical diagnosis and human-computer interaction. Mining human Interaction in meetings is very helpful to determine reactions of persons in different scenarios. Behavior denotes the nature of the person and mining helps to examine the exhibition of one’s opinion. This research work presents a novel technique to mine frequent patterns of human interaction based on the meetings. In this proposed approach, human interaction flow in a discussion session is denoted as a tree. Tree-based interaction mining of Weighted Interesting Pattern mining algorithms are designed to analyze the structures of the trees and to extract interaction flow patterns. The experimental results shows that several interesting patterns are efficiently extracted which are useful for the interpretation of human behavior in meeting discussions, such as determining frequent interactions, typical interaction flows, and relationships between different types of interaction.

A novel noise filter based on interesting pattern mining for bag-of-features images

Improving the quality of image data through noise filtering has gained more attention for a long time. To date, many studies have been devoted to filter the noise inside the image, while few of them focus on filtering the instance-level noise among normal images. In this paper, aiming at providing a noise filter for bag-of-features images, (1) we first propose to utilize the cosine interesting pattern to construct the noise filter; (2) then we prove that to filter noise only requires to mine the shortest cosine interesting patterns, which dramatically simplifies the mining process; (3) we present an in-breadth pruning technique to further speed up the mining process. Experimental results on two real-life image datasets demonstrate effectiveness and efficiency of our noise filtering method.

Interesting pattern mining in multi-relational data

Mining patterns from multi-relational data is a problem attracting increasing interest within the data mining community. Traditional data mining approaches are typically developed for single-table databases, and are not directly applicable to multi-relational data. Nevertheless, multi-relational data is a more truthful and therefore often also a more powerful representation of reality. Mining patterns of a suitably expressive syntax directly from this representation, is thus a research problem of great importance. In this paper we introduce a novel approach to mining patterns in multi-relational data. We propose a new syntax for multi-relational patterns as complete connected subsets of database entities. We show how this pattern syntax is generally applicable to multi-relational data, while it reduces to well-known tiles " Geerts et al. (Proceedings of Discovery Science, pp 278---289, 2004)" when the data is a simple binary or attribute-value table. We propose RMiner, a simple yet practically efficient divide and conquer algorithm to mine such patterns which is an instantiation of an algorithmic framework for efficiently enumerating all fixed points of a suitable closure operator "Boley et al. (Theor Comput Sci 411(3):691---700, 2010)". We show how the interestingness of patterns of the proposed syntax can conveniently be quantified using a general framework for quantifying subjective interestingness of patterns "De Bie (Data Min Knowl Discov 23(3):407---446, 2011b)". Finally, we illustrate the usefulness and the general applicability of our approach by discussing results on real-world and synthetic databases.