Erasable Patterns Research Articles

Advanced incremental erasable pattern mining from the time-sensitive data stream

Pattern mining has been actively advanced and studied in order to process data that is generated in real time, called incremental data. Erasable pattern mining is a concept that extracts erasable patterns as one of the fields belonging to pattern mining. Erasable patterns have useful information in terms of removal, unlike patterns found by other pattern mining methods. The former erasable pattern mining methods, which target incremental data, regard the importance of data identically. However, in real life, recent data are more important and useful than old data, so erasable pattern mining should also process the latest data more significantly in consideration of the feature of real-world data. For this reason, we propose an erasable pattern mining algorithm using an efficient data structure from the time-sensitive data stream. Since the suggested technique performs erasable pattern mining based on a damped window model, it is possible to relatively prioritize the importance of the recent data over the previous data. In addition, it is possible to quickly reflect and efficiently analyze the newly added incremental data due to a list based data structure of the proposed technique. The results of the various performance tests, which are conducted to compare our algorithm with state-of-the-art algorithms, are analyzed, and the analyses indicate that our approach is more efficient than the other latest approaches.

IME: Efficient list-based method for incremental mining of maximal erasable patterns

Erasable pattern mining can help factories facing a financial crisis increase productivity by identifying and eliminating unprofitable products. The Flag-GenMax-EI algorithm extracts Maximal Erasable Itemsets (MEIs); however, it does not support dynamic data. In practice, many applications create databases incrementally. Using the Flag-GenMax-EI algorithm to mine maximal erasable patterns from incremental databases is clearly very costly because it must be run each time. In this paper, an efficient method called IME is proposed for incremental mining of maximal erasable patterns. IMEI-List and IMEP-List are two new data structures introduced by the proposed method. These lists allow the algorithm to update all tree nodes without rescanning the updated database (original database + new database) and recreating the nodes. This is the first study of incremental mining of maximal erasable patterns. Extensive experimental results on dense and sparse incremental data show that the proposed algorithm improves scalability. It extracts MEIs much faster than the Flag-GenMax-EI algorithm in different modes of database update.

An efficient approach for mining maximized erasable utility patterns

As a method of extracting important information in the real world, the field of pattern mining has been actively studied. In particular, in industrial fields such as factories, it is also important to find patterns with low value and remove them in order to overcome the economic crisis. Erasable pattern mining has emerged to solve the concern. Traditional erasable pattern mining targeted a binary database containing only information on the existence of items. However, in the real world, data has a variety of properties, such as quantity and profit. Considering these properties makes it possible for more meaningful result patterns to be mined. Therefore, we propose a maximized erasable pattern mining algorithm that uses a list structure as a data structure and takes into account the quantity and price of the item. The proposed method considers the amount and relative benefit of each item to estimate the profit of a transaction and extracts the maximized erasable patterns considering the user’s preference. Moreover, the list structure utilized in the proposed method contains only the essential information for the mining process, which not only allows the mining to be performed efficiently, but also reduces memory usage. Using various real-world and synthetic datasets, we evaluate runtime, memory efficiency, and scalability of the suggested technique and state-of-the-art competitors. The evaluation results demonstrate that the proposed method performs better.

Advanced uncertainty based approach for discovering erasable product patterns

It is uncertain whether products will be manufactured as defective products. Producing the defective products causes the waste of time and finance in the industrial fields. When the financial crisis occurs in the manufacturing plants, production lines of the less profitable products should be stopped. To overcome the financial crisis, it is required to find the production lines that manufacture the less profitable commodities and are likely to produce the faulty merchandises and remove the corresponding production lines in order to increase the profit. Erasable pattern mining has been suggested to analyze and solve the financial problems by discovering patterns with low profit. As databases become more diverse due to the environment and characteristics of data, demands for erasable pattern mining that processes the various databases increase. An uncertain database, which is one of diverse types of databases, includes the probability related to the item, such as the probability of existence and the probability of defects. Erasable pattern mining, which considers the probability of each item, can discover pattern results which are more suitable for purpose of users. Motivated by this, we propose an efficient tree-based erasable pattern mining algorithm from the uncertain database. Considering the probability of the item, the suggested algorithm extracts erasable patterns with low profits from the uncertain database. Since both profit of the product and the probability of the item are considered, the discovered patterns are more meaningful than the patterns found by the existing erasable pattern mining. Moreover, the proposed method improves the mining operations by utilizing a list structure as well as a tree structure. We performed a performance evaluation on diverse real and synthetic datasets in order to prove the proposed algorithm outperforms the existing erasable pattern mining algorithms. The differences in runtime, memory usage, and scalability are shown through the comparison of performance with other state-of-the-art algorithms and the suggested algorithm.

A Fast Algorithm for Mining Top-Rank-k Erasable Closed Patterns

The task of mining erasable patterns (EPs) is a data mining problem that can help factory managers come up with the best product plans for the future. This problem has been studied by many scientists in recent times, and many approaches for mining EPs have been proposed. Erasable closed patterns (ECPs) are an abbreviated representation of EPs and can be considered condensed representations of EPs without information loss. Current methods of mining ECPs identify huge numbers of such patterns, whereas intelligent systems only need a small number. A ranking process therefore needs to be applied prior to use, which causes a reduction in efficiency. To overcome this limitation, this study presents a robust method for mining top-rank-k ECPs in which the mining and ranking phases are combined into a single step. First, we propose a virtual-threshold-based pruning strategy to improve the mining speed. Based on this strategy and dPidset structure, we then develop a fast algorithm for mining top-rank-k ECPs, which we call TRK-ECP. Finally, we carry out experiments to compare the runtime of our TRK-ECP algorithm with two algorithms modified from dVM and TEPUS (Top-rank-k Erasable Pattern mining Using the Subsume concept), which are state-of-the-art algorithms for mining top-rank-k EPs. The results for the running time confirm that TRK-ECP outperforms the other experimental approaches in terms of mining the top-rank-k ECPs.

Erasable pattern mining based on tree structures with damped window over data streams

Several pattern mining methods have been proposed to process dynamic data streams because the data generated in industrial fields is continually accumulated. Erasable pattern mining techniques for processing dynamic data streams are needed to discover erasable patterns from dynamic data streams. In previous erasable pattern mining approaches suggested for dynamic data streams, all data are considered to have the same importance regardless of its timestamp. However, dynamic data streams have the characteristic that the new data is relatively more significant than the old data. In erasable pattern mining, one of the desired techniques is an approach in consideration of such characteristic of data streams. For this reason, we propose an erasable pattern mining algorithm over dynamic data streams based on the damped window model. Since the suggested technique considers the new data more important than the previous data, it can find more useful erasable patterns. In addition, erasable pattern mining based on the damped window model is conducted efficiently by employing the tree and table structures. In performance test, we present that our pruning techniques remove unnecessary operations related to invalid erasable patterns efficiently from damped-window-based data streams. Performance evaluation results using real datasets and synthetic datasets show that the proposed approach has good performance with regard to as execution time, pattern generation, and scalability by comparing between the suggested technique and the state of the art algorithms.

Efficient approach for incremental weighted erasable pattern mining with list structure

Erasable pattern mining is one of the important fields of frequent pattern mining. It diagnoses and solves the economic problems that arise in the manufacturing industry. The real-world database is continually accumulated over time, and each item has a different importance. Therefore, if we use conventional erasable pattern mining without considering the characteristics of the real-world database, less meaningful patterns can be extracted. Also, when mining a real-world database, the algorithm must be able to process operations quickly and efficiently. In this paper, in order to meet these requirements, we propose an algorithm which is implemented as a list structure for mining erasable patterns in an incremental database with weighted condition. Compared to existing state-of-the-art mining algorithms, the proposed algorithm performs pattern pruning by applying weighted condition to a dynamic database, so it extracts fewer candidate patterns and shows fast performance. We test our algorithms and the algorithms previously presented with various real datasets and synthetic datasets and obtained results such as run time, memory usage, scalability, and accuracy tests. By analyzing and comparing these experimental results, we show that the proposed algorithm has outstanding performance.

Amenability of groups is characterized by Myhill's Theorem. With an appendix by Dawid Kielak

We prove a converse to Myhill's "Garden-of-Eden" theorem and obtain in this manner a characterization of amenability in terms of cellular automata: A group G is amenable if and only if every cellular automaton with carrier G that has gardens of Eden also has mutually erasable patterns. This answers a question by Schupp, and solves a conjecture by Ceccherini-Silberstein, Machì and Scarabotti. Furthermore, for non-amenable G the cellular automaton with carrier G that has gardens of Eden but no mutually erasable patterns may also be assumed to be linear. An appendix by Dawid Kielak shows that group rings without zero divisors are Ore domains precisely when the group is amenable, answering a conjecture attributed to Guba.

High strength dual-crosslinked hydrogels with photo-switchable color changing behavior

Herein, high strength dual-crosslinked hydrogels with photo-switchable color changing behavior were designed and fabricated. In the mixture solution of α-cyclodextrin (α-CD), α-cyclodextrin-spiropyran (α-CD-SP), and poly(ethylene glycol) diacrylate (PEGDA), the PEGDA chains first threaded into the cavity of α-CD/α-CD-SP and form inclusion complexes to generate the physical gel; then, the as-prepared physical gels were subjected to photopolymerization to form a covalently crosslinked network. With the contribution of both no-covalent and covalent bond, the dual-crosslinked hydrogels possess high tensile strength, which is about fifteen times that of the pure PEG hydrogel. And due to the sacrifice of no-covalent bond, hydrogels showed high energy dissipation ratio, up to 75.3% and high fatigue resistance during loading-unloading tests. While the ingenious introduction of SP moieties, enables the as-prepared gels photo-switchable spatiotemporal color changing behavior reversibly. Erasable patterns on hydrogels could be written using UV light and erased by Vis light for multiple cycles. This flexible hydrogel with good physical performance shows high potential in photochromic flexible wearable devices.

Advanced approach of sliding window based erasable pattern mining with list structure of industrial fields

As one of the various data mining areas, erasable pattern mining is a method for solving financial problems that can be caused in various industrial fields. Since the concept of erasable pattern mining was proposed, various relevant techniques have been developed. Dealing with dynamic stream data is an important issue in different data mining areas including erasable pattern mining. In order to deal with partial data with the latest information, a sliding window-based approach was proposed. However, the method still has performance limits in many cases because of its inefficient data structures and mining techniques. Motivated by this problem, we propose a new efficient erasable pattern mining algorithm based on the sliding window that can guarantee robustness and reliability and deal with the latest stream data more effectively. In addition, the newly proposed list data structure and mining techniques allow the proposed method to extract erasable patterns in a more efficient way. Results of empirical experiments using well-known benchmark datasets guarantee that the proposed algorithm outperforms previous state-of-the-art approaches in various aspects.

SPPC: a new tree structure for mining erasable patterns in data streams

Discovering Erasable Patterns (EPs) consists of identifying product parts that will produce a small profit loss if their production is stopped. It is a data mining problem that has attracted the attention of numerous researchers in recent years due to the possibility of using EPs to reduce profit loss of manufacturers. Though, many algorithms have been designed to mine EPs, an important limitation of state-of-the-art EP mining algorithms is that they are batch algorithms, that is, they are designed to be applied on static databases. But in real-life applications, databases are dynamic, as they are constantly updated by adding or removing products and parts. To be informed about EPs in real-time, traditional EP mining algorithms must be applied over and over again on a database. This is inefficient as those algorithms are always applied from scratch without taking advantage of results generated by previous executions. Considering this important drawback of previous work for handling real-life dynamic data, this paper proposes an efficient algorithm named MSPPC for mining EPs in data streams. It relies on a novel tree structure named SPPC (Streaming Pre-Post Code) tree, which extends the WPPC tree structure for maintaining a compact tree representation of EPs in a data stream. Experimental results show that the designed MSPPC algorithm outperforms the state-of-the-art batch MERIT and dMERIT algorithms when they are run in batch mode using a sliding-window. Besides, the proposed algorithm is also faster than the state-of-the-art algorithms for mining EPs, namely MERIT, dMERIT + , MEI and EIFDD.

Efficient algorithms for mining top-rank-k erasable patterns using pruning strategies and the subsume concept

Mining erasable patterns (EPs) is one of the emerging tasks in data mining which helps factory managers to establish plans for the development of new systems of products. However, systems usually face the problem of many EPs. Therefore, the problem of mining top-rank-k EPs, and an algorithm for mining these using the PID_List structure named VM, were proposed in 2013. In this paper, we propose two efficient methods, named the TEP (Top-rank-k Erasable Pattern mining) and TEPUS (Top-rank-k Erasable Pattern mining Using the Subsume concept) algorithms, for mining top-rank-k EPs. The TEP algorithm uses the dPidset structure to reduce the memory usage and a dynamic threshold pruning strategy to accelerate the mining process. The TEPUS algorithm is the extension of the TEP algorithm using the subsume concept and the index strategy to further speed up the mining time and reduce the memory usage. Finally, we conduct an experiment to compare the mining time, memory usage and scalability of TEP, TEPUS and two state-of-the-art algorithms (VM and dVM) for mining top-rank-k EPs. Our performance studies show that TEPUS outperforms TEP, VM and dVM.

Laser-induced erasable patterns in a N* liquid crystal on an iron doped lithium niobate surface.

A chiral nematic (N*) liquid crystal (LC) was hybridized with a z-cut iron doped lithium niobate (Fe:LN) substrate and exposed with a focused continuous wave diode laser beam. The N* LC layer was confined with a cover glass to provide a homogeneous LC layer thickness. Two distinct kinds of test cells were investigated, one with an uncoated glass covering slip and one with an indium tin oxide (ITO) coated cover glass. Photo generated electric fields (generated in the Fe:LN) resulted in a localized defect formation and textural transitions in the N* LC. Due to field confinement, the field induced responses were more localized in samples with ITO coated cover glasses. By scanning the laser beam on programmed trajectories, formation of persistent patterns could be achieved in the N* LC layer. Polarized optical microscopy of the exposed samples revealed that these patterns consisted of adjacent circular Frank-Pryce defects. Exposure with a slightly defocused laser beam could be applied selectively to erase these patterns. Thus, a promising method is reported to generate reconfigurable patterns, photonic motives, and touch sensitive devices in a hybridized N* LC with micron accuracy.

Single-pass based efficient erasable pattern mining using list data structure on dynamic incremental databases

Many different approaches of data mining have been proposed to satisfy various demands of users. Erasable pattern mining is one of the interesting areas in frequent pattern mining, which was proposed to diagnose and solve financial problems caused in industrial fields. Since its original concept emerged, various relevant approaches have been devised. Analyzing incremental data becomes more important because interesting data are continually accumulated in various application fields including industrial areas. For this reason, an incremental method for erasable pattern mining has also been suggested in order to reflect such a trend. Since incremental data become gradually larger and more complicated with the passage of time, it is important to process such data as quickly and efficiently as possible. However, the previous method has limitations in this respect. Motivated by this challenge, we propose a new incremental erasable pattern mining algorithm including new data structures and mining techniques for efficient incremental data processing. We also demonstrate that the proposed method outperforms previous state-of-the-art approaches through extensive, empirical performance tests.

Efficient Algorithms for Mining Erasable Closed Patterns From Product Datasets

Finding knowledge from large data sets to use in intelligent systems becomes more and more important in the Internet era. Pattern mining, classification, text mining, and opinion mining are the topical issues. Among them, pattern mining is an important issue. The problem of mining erasable patterns (EPs) has been proposed as a variant of frequent pattern mining for optimizing the production plans of factories. Several algorithms have been proposed for effectively mining EPs. However, for large threshold values, many EPs are obtained, leading to large memory usage. Therefore, it is necessary to mine a condensed representation of EPs. This paper first defines erasable closed patterns (ECPs), which can represent the set of EPs without information loss. Then, a theorem for fast determining ECPs based on dPidset structure is proposed and proven. Next, two efficient algorithms [erasable closed pattern mining (ECPat) and dNC_Set based algorithm for erasable closed pattern mining (dNC-ECPM)] for mining ECPs based on this theorem are proposed. Experimental results show that ECPat is the best method for sparse data sets, while dNC-ECPM algorithm outperforms ECPat algorithm and a modified mining erasable itemsets algorithm in terms of the mining time and memory usage for all remaining data sets.

Sliding window based weighted erasable stream pattern mining for stream data applications

As one of the variations in frequent pattern mining, erasable pattern mining discovers patterns with benefits lower than or equal to a user-specified threshold from a product database. Although traditional erasable pattern mining algorithms can perform their own mining operations on static mining environments, they are not suitable for dealing with dynamic data stream environments. In such dynamic data streams, algorithms have to process them immediately with only one database scan in order to consider characteristics of data stream mining. However, previous tree-based erasable pattern mining methods have difficulty in processing dynamic data streams because they need two or more database scans to construct their own tree structures. In addition, they do not also consider specific information of each item within a product database, but they need to conduct mining operations considering such additional information of the items in order to find more useful erasable pattern results. For this reason, in this paper, we propose a weighted erasable pattern mining algorithm suitable for sliding window-based data stream environments. The algorithm employs tree and list data structures for more efficient mining processes and solves the problems of previous erasable pattern mining approaches by using a sliding window-based stream processing technique and an item weight-based pattern pruning method. We compare performance of the proposed algorithm to state-of-the-art tree-based approaches with respect to various real and synthetic datasets. Experimental results show that our method is more efficient and scalable than the competitors in terms of runtime, memory, and pattern generation.

Mining weighted erasable patterns by using underestimated constraint-based pruning technique

Erasable pattern mining is one of the variations in frequent pattern mining, and its main goal is to maximize the production capacity of manufacturing industries by quickly overcoming the financial...

Mining weighted erasable patterns by using underestimated constraint-based pruning technique

Erasable pattern mining is one of the variations in frequent pattern mining, and its main goal is to maximize the production capacity of manufacturing industries by quickly overcoming the financial crises that can occur in industries. However, existing erasable pattern mining approaches utilize only profit information for each product, but do not consider distinct weights of items organizing the products. In addition, previous algorithms spend much time and space mining erasable patterns due to their inefficient pattern mining methods. In a real-life environment, considering weights of items in each product can be more important compared to calculating product profits only. For this reason, we propose a novel algorithm for mining weighted erasable patterns by considering the distinct weight of each item. Moreover, we discuss both discovering weighted erasable patterns and minimizing the resource availability of erasable pattern mining processes utilizing weight conditions. Especially, our approach has advantages for time and space resource consumption compared to existing approaches because our algorithm uses a pattern pruning method and stores item information included in product databases by using both compact tree and hash list structures. We present performance evaluation utilizing both real and synthetic datasets to demonstrate the efficiency of our algorithm.

Transparent, Luminescent, Antibacterial and Patternable Film Forming Composites of Graphene Oxide/Reduced Graphene Oxide

Multifunctional graphene oxide/reduced graphene oxide (GO/RGO) composites were prepared through electrostatic interaction using biocompatible ingredients. Different functionalities were added to GO/RGO by anchoring materials such as native lactoferrin (NLf), NLf protected Au clusters (designated as Au@NLf), chitosan (Ch) and combinations thereof. Anchoring of Ch and NLf enhances the antibacterial property of RGO/GO. The addition of Ch to RGO/GO not only helped in forming stable dispersions but also helped in fabricating large (cm(2)) area films through a simple solvent evaporation technique. Functionalities such as photoluminescence were added to Ch-RGO/GO composites by anchoring Au@NLf on it. The composites thus formed showed stable luminescence in presence of various metal ions in the solid state. The composite showed reasonable stability against pH and temperature variations as well. The as-prepared films were transparent and the transparency could be modulated by controlling the concentration of RGO/GO in the composite. The antibacterial property and ability to form stable thin films may provide an opportunity to use such composites for medical and environmental remediation applications as well. Erasable patterns were fabricated on the film by stamping required patterns under compressive pressure. Luminescent patterns can be inscribed on the film and can be erased by simply wetting it. Such films with erasable information may be useful for security applications.

Gardens of Eden and amenability on cellular automata

We prove a converse to the “Garden-of-Eden” theorem by Ceccherini-Silberstein, Machì and Scarabotti, and to a theorem by Meyerovitch, yielding two new characterizations of amenable groups. The following are equivalent: the group G is amenable; all cellular automata living on G that admit mutually erasable patterns also admit gardens of Eden; all cellular automata living on G that do not preserve Bernoulli measure admit gardens of Eden. This solves in particular Conjecture 6.2 (1) in [2].