Sequential Pattern Generation Research Articles

A Data Mining Framework for Maintenance Prediction of Faulty Products Under Warranty

Predicting the sequential patterns of maintenance activities (replacement or repair) with the needed spare parts for faulty products becomes the main challenge to maintenance engineers. This research, therefore, develops a data mining framework for predicting the integrated sequential patterns of maintenance activities and identifying the classification of the frequent components’ spare parts for faulty products under warranty. In this framework, a large data set was mined for products under warranty including product attributes, maintenance activities, and spare parts. Then, data mining techniques were performed to determine the frequent sequence pattern of maintenance activities, involving: the selection of monthly maintenance activities, generalized sequential patterns (GSP), generation of association rules, and rule-based classification with/without considering product attributes. The frequent sequential patterns were validated using testing data. Further, the GSP was applied to determine the frequent sequential patterns of spare parts. Finally, integrated sequential patterns were generated for maintenance activities and spare parts. A case study of water-cooled chiller products was deployed to illustrate the developed framework. The effectiveness of the framework was illustrated with the warranty data mining for a water-cooled chiller. In conclusion, the proposed framework allows maintenance engineers to extract hidden knowledge regarding sequential patterns of maintenance planning and provides valuable information for maintenance prediction.

Enhanced parallel mining algorithm for frequent sequential rules

Sequential rule mining is an important data mining technique that discovers relationships between occurrences of sequential patterns. The main challenge is to avoid time-consuming, especially in large search spaces. This can be achieved through developing an efficient sequential rule mining algorithm without redundancy in a long sequence dataset. In this paper, an algorithm named PNRD-CloGen is proposed. It can be used to mine sequential rules from both frequent closed dynamic bit vectors with sequential generator patterns at the same time. It helps in speedily eliminating uninteresting candidates and compact the representations. Additionally, we apply a parallel approach utilizing multi-core architecture. An experimental evaluation was performed using five real sequence datasets: BMSWebView1, Sign, FIFA, Korsarak, and MSNBC. The proposed algorithm has been compared with two non-redundant sequential rule algorithms called: TRuleGrowth, and NRD-DBV algorithm. Experimental results show the time saving, especially for large sequence datasets and low minimum support threshold.

Fast generation of sequential patterns with item constraints from concise representations

Constraint-based frequent sequence mining is an important and necessary task in data mining since it shows results very close to the requirements and interests of users. Most existing algorithms for performing this task are based on a traditional approach that mines patterns directly from a sequence database (SDB). However, in fact, SDBs are often very large. The algorithms thus often exhibit poor performance because the number of generated candidates and the search space are enormous, especially for low minimum support thresholds. In addition, these algorithms must read an SDB again when a constraint is changed by the user. In the context of frequently varied constraints, repeatedly scanning SDBs consume much time. To address this issue, we propose a novel approach for generating frequent sequences with various constraints from the two sets of frequent closed sequences ($$ {{\mathcal{F}}{\mathcal{C}}{\mathcal{S}}} $$) and frequent generator sequences ($$ {{\mathcal{F}}{\mathcal{G}}{\mathcal{S}}} $$), which are the concise representations of the set $$ {{\mathcal{F}}{\mathcal{S}}} $$ of all frequent sequences. The proposed approach is based on novel theoretical results that show an explicit relationship between $$ {{\mathcal{F}}{\mathcal{S}}} $$ and these two sets and have been strictly proved. The approach is then used to develop an efficient algorithm named MFS-IC for quickly generating frequent sequences with item constraints, a task that has many real-life applications. Extensive experiments on real-life and synthetic databases show that the proposed MFS-IC algorithm outperforms state-of-the-art algorithms, which directly mine frequent sequences with constraints from an SDB, in terms of runtime, memory usage and scalability.

Mining Sequential Patterns of Diseases Contracted and Medications Prescribed before the Development of Stevens-Johnson Syndrome in Taiwan

Medication is designed to cure diseases, but serious risks can arise from severe adverse drug reactions (ADRs). ADRs can lead to emergency room visits and hospitalization, straining healthcare resources and, thus, they have strong implications for public health. Stevens–Johnson Syndrome (SJS) is one ADR and comprises the highest proportion of all drug relief cases in Taiwan. Pharmacovigilance involves the collection, detection, assessment, monitoring, and prevention of ADRs, including SJS. Most medical specialists are not fully aware of the risk of drug-induced SJS. Consequently, various drugs may be prescribed to susceptible patients for a great variety of diseases and, in turn, cause SJS. In this research, medical records of SJS patients were retrieved from the Taiwan National Health Insurance Research Database, and the Generalized Sequential Patterns (GSP) algorithm was used to find the sequential patterns of diseases before SJS onset. Then we mined the sequential patterns of medications prescribed in each disease pattern. Afterwards, we detected significant associations of each pattern of diseases and medications prescribed among age groups with statistical analysis. We found that, first, most patients developed SJS after being prescribed the causative medications fewer than four times. Second, Respiratory System Diseases (RSDs) appeared in disease sequential patterns of all lengths. Patterns involving RSDs were more frequent than others. Third, NSAIDs, H2-antagonists for peptic ulcer, penicillin antibiotics, theophylline bronchodilators, and cephalosporin antibiotics were the most frequent medications prescribed. Fourth, we found that patients in certain age groups had higher risks of developing SJS. This study aimed to mine the sequential patterns of diseases contracted and medications prescribed before patients developed SJS in Taiwan. This useful information can be provided to physicians so that they can stop the administration of suspected drugs to avoid evolution towards more severe cases.

Balanced approach for the two-dimensional rectangular guillotine cutting stock problem with setup cost

The main optimization objective of two-dimensional rectangular guillotine cutting stock problem (2DRGCSP) is minimizing stock cost. However, setup cost is also needed to be considered as an important factor in the real manufacturing process. A sequential pattern generation algorithm with rectangular homogenous strips (SPGA-RHSs) is presented to generate a cutting plan based on each single stock. A better cutting pattern considering both stock cost and setup cost is obtained with the cost balance utilization (CBU). The balanced approach combining SPGA-RHSs and CBU is used to achieve a current optimal cutting plan for 2DRGCSP with the minimum sum of stock and setup cost. The computational results prove that the proposed balanced approach has good optimization performance for 2DRGCSP with setup cost.

Efficient algorithms for simultaneously mining concise representations of sequential patterns based on extended pruning conditions

The concise representations of sequential patterns, including maximal sequential patterns, closed sequential patterns and sequential generator patterns, play an important role in data mining since they provide several benefits when compared to sequential patterns. One of the most important benefits is that their cardinalities are generally much less than the cardinality of the set of sequential patterns. Therefore, they can be mined more efficiently, use less storage space, and it is easier for users to analyze the information provided by the concise representations. In addition, the set of all maximal sequential patterns can be utilized to recover the complete set of sequential patterns, while closed sequential patterns and sequential generators can be used together to generate non-redundant sequential rules and to quickly recover all sequential patterns and their frequencies. Several algorithms have been proposed to mine the concise representations separately, i.e., each of them has been designed to discover only a type of the concise representation. However, they remain time-consuming and memory intensive tasks. To address this problem, we propose three novel efficient algorithms named FMaxSM, FGenCloSM and MaxGenCloSM to exploit only maximal sequential patterns, to simultaneously mine both the sets of closed sequential patterns and generators, and to discover all three concise representations during the same process. To our knowledge, MaxGenCloSM is the first algorithm for concurrently mining the three concise representations of sequential patterns. The proposed algorithms are based on two novel local pruning strategies called LPMAX and LPMaxGenClo that are designed to prune non-maximal, non-closed and non-generator patterns earlier and more efficiently at two and three successive levels of the prefix tree without subsequence relation checking. Extensive experiments on real-life and synthetic databases show that FMaxSM, FGenCloSM and MaxGenCloSM are up to two orders of magnitude faster than the state-of-the-art algorithms and that the proposed algorithms consume much less memory, especially for low minimum support thresholds and for dense databases.

A balance approach for the one-dimensional multiple stock size cutting stock problem with setup cost

A balance approach is presented to solve one-dimensional multiple stock size cutting stock problem with setup cost. The approach first utilizes a sequential pattern generation algorithm to generate a series of cutting plans based on each stock size, respectively. Then, a measure standard of cost balance utilization is used to select a current optimized cutting pattern from a cutting plan corresponding to each stock size. All item demands are dealt by the previous two steps to obtain many optimized cutting plans, and an ideal cutting plan is extracted according to the minimum sum of stock and setup costs at last. The approach is applied to two tests, and the computational results demonstrate that it possesses good cost adaptability and optimization performance.

Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure.

Acute hypotension is a significant risk factor for in-hospital mortality at intensive care units. Prolonged hypotension can cause tissue hypoperfusion, leading to cellular dysfunction and severe injuries to multiple organs. Prompt medical interventions are thus extremely important for dealing with acute hypotensive episodes (AHE). Population level prognostic scoring systems for risk stratification of patients are suboptimal in such scenarios. However, the design of an efficient risk prediction system can significantly help in the identification of critical care patients, who are at risk of developing an AHE within a future time span. Toward this objective, a pattern mining algorithm is employed to extract informative sequential contrast patterns from hemodynamic data, for the prediction of hypotensive episodes. The hypotensive and normotensive patient groups are extracted from the MIMIC-II critical care research database, following an appropriate clinical inclusion criteria. The proposed method consists of a data preprocessing step to convert the blood pressure time series into symbolic sequences, using a symbolic aggregate approximation algorithm. Then, distinguishing subsequences are identified using the sequential contrast mining algorithm. These subsequences are used to predict the occurrence of an AHE in a future time window separated by a user-defined gap interval. Results indicate that the method performs well in terms of the prediction performance as well as in the generation of sequential patterns of clinical significance. Hence, the novelty of sequential patterns is in their usefulness as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients.

Efficiently Mining Sequential Generator Patterns Using Prefix Trees

Mining long frequent sequences that contain a combinatorial number of frequent subsequences or using very low support thresholds to mine sequential patterns is usually both time-and memory-consumin...

A multi-phase approach for classifying multi-dimensional sequence data

We present a new methodology for sequential classification, which employs sequential pattern generation and classification, in a two-stage process. In the first phase, a set of sequential patterns are generated from multi-dimensional sequence data. We proposes a novel method for inducing multi-dimensional sequential patterns with the use of Hellinger measure. The importance of each sequential pattern is also calculated. In the second phase, the generated sequential patterns are used for classifying multi-dimensional sequence data. A number of theorems are proposed to reduce the computational complexity of generating sequential patterns. The proposed method is tested on some synthesized sequence databases.

Efficiently Mining Sequential Generator Patterns Using Prefix Trees

Mining long frequent sequences that contain a combinatorial number of frequent subsequences or using very low support thresholds to mine sequential patterns is usually both time-and memory-consuming. The mining of closed sequential patterns, sequential generator patterns, and maximum sequences has been proposed to overcome this problem. Sequential generator patterns, when used together with closed sequential patterns, can provide additional information that closed sequential patterns alone cannot provide. Mining sequential generator patterns is thus an important task in data mining as well. This paper proposes an algorithm called MSGP-PreTree for mining all sequential generator patterns based on the prefix-tree structure. The algorithm uses the characteristics of sequential generator patterns and sequence extensions to efficiently perform a depth-first search on a prefix tree. It also uses a vertical approach to list and count the supports of sequences based on the prime block encoding approach for representing candidate sequences and determining the frequencies of candidates. Besides, the search space of the MSGP-PreTree algorithm is much smaller than those of other algorithms because two pruning strategies are applied. Experimental results conducted on synthetic and real databases show that the proposed algorithm is effective than a previous one.

An efficient method for mining non-redundant sequential rules using attributed prefix-trees

Mining sequential generator patterns and mining closed sequential patterns are important to sequence mining. They address difficulties in the mining of sequential patterns by reducing the number of sequential rules, and the results are often used to generate non-redundant sequential rules. This paper proposes an efficient method for mining non-redundant sequential rules from an attributed prefix-tree. The proposed method has two phases. In the first phase, it builds a prefix-tree that stores all the sequential patterns from a given sequence database. Then in the second phase, it mines non-redundant sequential rules from this prefix-tree. In the prefix-tree building process, each node on the prefix-tree has a field that indicates whether this node is a minimal sequential generator pattern, and another field that indicates whether this node is a closed sequential pattern. By traversing the prefix-tree, non-redundant sequential rules can be easily mined from a minimal sequential generator pattern X to a closed sequential pattern Y such that X is a prefix of Y. In addition, a good pruning mechanism is proposed to reduce the search space and the execution time in the mining process. Experimental results show that the proposed method is more efficient than existing methods in mining non-redundant sequential rules.

An efficient algorithm for mining sequential generator pattern using prefix trees and hash tables

Mining long frequent sequences that contain a combinatorial number of frequent subsequences or using very low support thresholds to mine sequential patterns is both time-and memory-consuming. The mining of closed sequential patterns, sequential generator patterns, and maximum sequences has been proposed to overcome this problem. This paper proposes an algorithm for generating all sequential generator patterns. This algorithm uses a vertical approach to listing and counting the support of sequence based on the prime block encoding approach to represent candidate sequences and determine the frequency for each candidate. The search space of the proposed algorithm is much smaller than those of other algorithms because super sequence frequency-based pruning and non-generator-based pruning are applied. Besides, hash tables are also used for fast checking the existed sequential generator patterns. Experimental results conducted on synthetic and real databases show that the proposed algorithm is effective.

An effective algorithm for mining closed sequential patterns and their minimal generators based on prefix trees

Sequential generator patterns and closed sequential patterns play an important role in data mining tasks. They are proposed to address difficult problems in mining sequential pattern and have often been used together to generate non-redundant rules. Based on their important role, this paper proposes an efficient algorithm called CloGen for mining closed sequential patterns and their minimal sequential generator patterns. The CloGen algorithm uses the parent-child relationship on prefix tree structure and inserts fields into each node on prefix tree to determine whether that is a minimal sequential generator pattern or closed sequential pattern. Experimental results show that the performance runtime of CloGen algorithm is much faster than that of other algorithms by more than one order of magnitude.

SeqGen: Mining Sequential Generator Patterns from Sequence Databases

SeqGen: Mining Sequential Generator Patterns from Sequence Databases

Fast Algorithms for Discovering Sequential Patterns in Massive Datasets

Problem statement: Sequential pattern mining is one of the specific data mining tasks, particularly from retail data. The task is to discover all sequential patterns with a user-specified minimum support, where support of a pattern is the number of data-sequences that contain the pattern. Approach: To find a sequence patterns variety of algorithm like AprioriAll and Generalized Sequential Patterns (GSP) were there. We present fast and efficient algorithms called AprioriAllSID and GSPSID for mining sequential patterns that were fundamentally different from known algorithms. Results: The proposed algorithm had been implemented and compared with AprioriAll and Generalized Sequential Patterns (GSP). Its performance was studied on an experimental basis. We combined the AprioriAllSID algorithm with AprioriAll algorithm into a Hybrid algorithm, called AprioriAll Hybrid. Conclusion: Implementation shows that the execution time of the algorithm to find sequential pattern depends on total no of candidates generated at each level and the time taken to scan the database. Our performance study shows that the proposed algorithms have an excellent performance over the best existing algorithms.

Pattern Generation by Network composed of Oscillators and Capacitive Couplers

Abstract This article proposes a network system composed of oscillators and capacitive couplers as an abstract neural circuit model for use in elucidating functional combinations of neural elements. The capacitive couplers produce changes in the interactions between the oscillators and enable various functions that the oscillators alone cannot realize. The functions added by these capacitive couplers include NOR-based logic operations, a falling edge detector, an astable multivibrator, and sequential pattern generation. The results of this study suggest that the incorporation of capacitive functions in an oscillatory network system is a beneficial means of extending the functionality of the system.

An effective algorithm for mining sequential generators

Mining frequent sequences patterns invokes the interests of many searchers. However, the result set of frequent sequences is tremendous. While frequent sequential generator patterns can compact the result set of frequent sequences greatly and are superior to the frequent sequential closed patterns in classification and model selection. The existing sequential generator patterns mining algorithm fails to make full use of the relationship between a sequence and its subsequence. And when mining the frequent sequential generators, the existing algorithms are ineffective and inefficient. In order to resolve this problem, an effective and efficient algorithm is presented. A lot of experiments about the performance on datasets are conducted. The results show that the algorithm proposed is better than the existing algorithms for mining the sequential generators.

Developing an efficient knowledge discovering model for mining fuzzy multi-level sequential patterns in sequence databases

Sequential pattern mining from sequence databases has been recognized as an important data mining problem with various applications. Items in a sequence database can be organized into a concept hierarchy according to taxonomy. Based on the hierarchy, sequential patterns can be found not only at the leaf nodes (individual items) of the hierarchy, but also at higher levels of the hierarchy; this is called multiple-level sequential pattern mining. In previous research, taxonomies based on crisp relationships between any two disjointed levels, however, cannot handle the uncertainties and fuzziness in real life. For example, Tomatoes could be classified into the Fruit category, but could be also regarded as the Vegetable category. To deal with the fuzzy nature of taxonomy, Chen and Huang developed a novel knowledge discovering model to mine fuzzy multi-level sequential patterns, where the relationships from one level to another can be represented by a value between 0 and 1. In their work, a generalized sequential patterns (GSP)-like algorithm was developed to find fuzzy multi-level sequential patterns. This algorithm, however, faces a difficult problem since the mining process may have to generate and examine a huge set of combinatorial subsequences and requires multiple scans of the database. In this paper, we propose a new efficient algorithm to mine this type of pattern based on the divide-and-conquer strategy. In addition, another efficient algorithm is developed to discover fuzzy cross-level sequential patterns. Since the proposed algorithm greatly reduces the candidate subsequence generation efforts, the performance is improved significantly. Experiments show that the proposed algorithm is much more efficient and scalable than the previous one. In mining real-life databases, our works enhance the model's practicability and could promote more applications in business.

A New Real-Time Function Test Generation System for Complex LSI Testing

This paper discusses the applications and architectural features of a sequential pattern generator designed for a general-purpose high-speed large-scale integration (LSI) test system. This system provides enhanced capabilities for testing communications-microprocessor-oriented LSI devices which require long test patterns and flexible input-output (I-O) control of individual pins. Salient features discussed are a 10 MHz random access memory with an 80-bit word size and controller capable of 100 ns subroutine calls and loops in unlimited numbers. Also, individual pin changes such as I-O states, timing, and waveform format at 10 MHz rates are de- scribed.