Extract Frequent Patterns Research Articles

Novel Algorithms CIPFP for Mining Frequent Patterns using Counting Inference from Probabilistic Databases and Future Possibilities

We consider the problem of discovering frequent item sets and association rules between items in a large database of transactional databases acquired under uncertainty. A probabilistic database considered here is one in which with each transaction associated is a probability, represents the confidence that the transaction will occur with given associated certainty. In this paper, we address the problem of the efficiency of the main phase of most data mining applications: The frequent pattern extraction. This problem is mainly related to the number of operations required for counting pattern supports in the database and we propose a new method, called counting inference probabilistic frequent pattern miner in probabilistic databases, this algorithm allows to perform as few support counts as possible. It is optimized to reduce the number of database scan as well as the number of patterns for which explicit support count is required. Using this method, the support of a pattern is determined without accessing the database whenever possible, using the supports of some of its sub-patterns called key patterns. This method was implemented in the CIPFP, counting inference based probabilistic frequent pattern mining algorithm that is an optimization of the simple and efficient Apriori algorithm. The goal is to transform all key patterns into non-key patterns as early as possible as for non-key-patterns database scan is not required at all. General Terms Data Mining, Big Data, Data Science, Association Rule Mining,

Evidential data mining: precise support and confidence

Associative classification has been shown to provide interesting results whenever of use to classify data. With the increasing complexity of new databases, retrieving valuable information and classifying incoming data is becoming a thriving and compelling issue. The evidential database is a new type of database that represents imprecision and uncertainty. In this respect, extracting pertinent information such as frequent patterns and association rules is of paramount importance task. In this work, we tackle the problem of pertinent information extraction from an evidential database. A new data mining approach, denoted EDMA, is introduced that extracts frequent patterns overcoming the limits of pioneering works of the literature. A new classifier based on evidential association rules is thus introduced. The obtained association rules, as well as their respective confidence values, are studied and weighted with respect to their relevance. The proposed methods are thoroughly experimented on several synthetic evidential databases and showed performance improvement.

Natural morphosyntax

Fluent speakers do not appear to have conscious knowledge of the linguistic categories and declarative rules that linguists use to describe grammar and that most psycholinguists have adopted for explaining language functioning. The implication derived in this paper is that these categories and rules are deprived of psychological reality. It is proposed that a psychologically real morphosyntax is concerned with sentence surface. The pragmatic framework and the semantic relational matrix at the onset of sentence production are converted directly into syntagmatic patterns, flexibly distributed along the sentence line. These patterns are reflected in probabilistic associations between words and sequences of words. Natural morphosyntax is learned incidentally through implicit procedural learning. Children extract frequent syntagmatic patterns from adapted adult input. The resulting knowledge is stored in procedural memory. The cortico-striatal -cerebellar system of the brain has the computational power necessary to deal with sentence sequential patterning and associative regularities.

An evolutionary algorithm for the discovery of rare class association rules in learning management systems

Association rule mining, an important data mining technique, has been widely focused on the extraction of frequent patterns. Nevertheless, in some application domains it is interesting to discover patterns that do not frequently occur, even when they are strongly related. More specifically, this type of relation can be very appropriate in e-learning domains due to its intrinsic imbalanced nature. In these domains, the aim is to discover a small but interesting and useful set of rules that could barely be extracted by traditional algorithms founded in exhaustive search-based techniques. In this paper, we propose an evolutionary algorithm for mining rare class association rules when gathering student usage data from a Moodle system. We analyse how the use of different parameters of the algorithm determine the rule characteristics, and provides some illustrative examples of them to show their interpretability and usefulness in e-learning environments. We also compare our approach to other existing algorithms for mining both rare and frequent association rules. Finally, an analysis of the rules mined is presented, which allows information about students' unusual behaviour regarding the achievement of bad or good marks to be discovered.

ACME: A scalable parallel system for extracting frequent patterns from a very long sequence

Modern applications, including bioinformatics, time series, and web log analysis, require the extraction of frequent patterns, called motifs, from one very long (i.e., several gigabytes) sequence. Existing approaches are either heuristics that are error-prone, or exact (also called combinatorial) methods that are extremely slow, therefore, applicable only to very small sequences (i.e., in the order of megabytes). This paper presents ACME, a combinatorial approach that scales to gigabyte-long sequences and is the first to support supermaximal motifs. ACME is a versatile parallel system that can be deployed on desktop multi-core systems, or on thousands of CPUs in the cloud. However, merely using more compute nodes does not guarantee efficiency, because of the related overheads. To this end, ACME introduces an automatic tuning mechanism that suggests the appropriate number of CPUs to utilize, in order to meet the user constraints in terms of run time, while minimizing the financial cost of cloud resources. Our experiments show that, compared to the state of the art, ACME supports three orders of magnitude longer sequences (e.g., DNA for the entire human genome); handles large alphabets (e.g., English alphabet for Wikipedia); scales out to 16,384 CPUs on a supercomputer; and supports elastic deployment in the cloud.

Distributed Cluster Based Association Rule Mining Approach

Association rule mining is one of the most important and well researched techniques of data mining. It aims to extract interesting correlations, frequent patterns, associations or casual structures among sets of items in the transaction databases or other data rep ositories. The discovery of association rules is an important data mining task for which many algorithms have been proposed. However, the efficiency of these algorithms needs to be improved to handle real world large datasets. In this paper, authors present an efficient method named Distributed Cluster Based Association Rule (CBAR). In the proposed Distributed CBAR method, the database is first partitioned between various nodes and then by using a count function, different size cluster tables are created by scanning the database. The proposed approach has reduced the need to scan the database. The experiment results show that Distributed CBAR has a superior performance over the existing FDM (Fast Distributed Algorithm for Mining Association Rules) approach. Keywords: Data Mining, Distributed Association Rule Mining, Cluster Table, Polling Sites.

랜드마크 윈도우 기반의 빈발 패턴 마이닝 기법의 분석 및 성능평가

ABSTRACT With the development of online service, recent forms of databas es have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment o f marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artific ial intelligence. Landmark window-based frequent pattern mining , one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, inste ad of all the data. In this paper, we analyze and evaluate the tec hniques of the well-known landmark window-based frequent patter n mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns a s soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reaso n, such algorithms are also called online mining approaches. We evaluat e and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms’ total runtime and average processing time per transaction. In addition, to compare the ef ficiency of storage structures between them, their maximum memo ry usage is also evaluated. Lastly, we show how stably the two alg orithms conduct their mining works with respect to the database s that feature gradually increasing items. With respect to the evaluat ion results of mining time and transaction processing, hMiner h as higher speed than that of Lossy counting. Since hMiner stores candidat e frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a l attice manner; thus, it has to search for multiple nodes in ord er to access the candidate frequent patterns. On the other hand, hMiner show s worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash’s bucket s, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner pres ents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is

A Survey Paper on Trajectory Pattern Mining for Pattern Matching Query

amount information of moving objects on road network is being collected with the help of various recent technologies. The tracking of these moving objects on road networks is becoming important because of it's application in various areas. Classification has been used for classifying various kinds of data sets like graph, text documents. However, there is a lack of study on data like trajectories on road networks. Data mining techniques, especially, sequential pattern mining can be used to extract frequent spatio-temporal patterns. Again it needs to confine the length of sequential patterns to ensure high efficiency. After extracting frequent sequential patterns in trajectories, classification can be applied to classify patterns which provide useful information in applications such as city and transportation planning, road construction, design, and maintenance, marketing sector. In this paper, whole pattern matching query concept is adopted after the classification to find total traffic volume on given trajectory edge. At the same time, user can find number of vehicles moving in one as well as in both directions on that particular trajectory. Keywordssequential patterns, frequent pattern based classification, location based services, pattern matching.

Multiple Buffering for Parallel Approximate Sequence Matching using Disk-based Suffix Tree on Multi-core CPU

AbstractSuffix trees, which are trie structures that present the suffixes of sequences (e.g., strings), are widely used for sequence search in different application domains such as, text data mining, bioinformatics and computational biology. In particular, suffix trees are useful in bioinformatics applications, because they can search similar sub-sequences and extract frequent sequence patterns efficiently. In recent years, efficient construction of a suffix tree that allows faster sequence searches has become one of the most important challenges, because the number and size of the data that are stored in sequence databases have been increasing exponentially. This paper proposes a novel parallelization model for approximate sequence matching that uses disk-based suffix trees, which are built on hard disks not on memory, on a multi-core CPU. In the proposed parallelization model, we divide an entire sequence database into two or more sub-databases called partitions. For each partition, we build a disk-based suffix tree and define a task as an approximate sequence matching on one disk-based suffix tree. Moreover, the proposed parallelization model involves a multiple buffering management system to avoid conflicts among CPU-cores. We evaluated the proposed parallelization model using an actual amino acid sequence database on a PC. The experimental results show a substantial improvement in computation performance.

Capturing and analyzing verbal and physical collaborative learning interactions at an enriched interactive tabletop

Interactive tabletops can be used to provide new ways to support face-to-face collaborative learning. A little explored and somewhat hidden potential of these devices is that they can be used to enhance teachers’ awareness of students’ progress by exploiting captured traces of interaction. These data can make key aspects of collaboration visible and can highlight possible problems. In this paper, we explored the potential of an enriched tabletop to automatically and unobtrusively capture data from collaborative interactions. By analyzing that data, there was the potential to discover trends in students’ activity. These can help researchers, and eventually teachers, to become aware of the strategies followed by groups. We explored whether it was possible to differentiate groups, in terms of the extent of collaboration, by identifying the interwoven patterns of students’ speech and their physical actions on the interactive surface. The analysis was validated on a sample of 60 students, working in triads in a concept mapping learning activity. The contribution of this paper is an approach for analyzing students’ interactions around an enriched interactive tabletop that is validated through an empirical study that shows its operationalization to extract frequent patterns of collaborative activity.

A New Approach for Extracting Closed Frequent Patterns and their Association Rules using Compressed Data Structure

mining, term frequent pattern extraction is largely used for finding out association rules. Generally association rule mining approaches are used as bottom-up or top-down approach on compressed data structure. In the past, different works proposed different approaches to mine frequent patterns from giving databases. In this paper, we propose a new approach by applying the closed & intersection approach using compressed data structure. We have used closed as bottom-up and intersection as top-down approach. This combined approach allows diminishing the search time by reducing database scan for finding out closed frequent patterns and their association rules. The time complexity of the proposed algorithm is less while the classical approach like a priori has taken more time for given items in the dataset. Experimental results show that our approach is more efficient and effective than a traditional apriori algorithm.

Anomaly detection in medical treatment to discover unusual patient management

The increasing availability of electronic medical records makes it possible to reconstruct patient treatment patterns adopted in a given clinical setting. Developing methods to detect deviations from these patterns may help to determine whether the management of a patient is unusual in some way. Using pattern mining techniques, our method extracts frequent patterns from a given dataset of treatment undergone by patients. Comparison is then made between frequent patterns of treatment and the domain medical knowledge, thus allowing the detection of two types of anomalies. The first type includes frequent patterns which deviate from accepted guidelines. These patterns can be evaluated and eventually fed back into improving the guidelines. The second type of anomalies comprises the anomalous cases that deviate from the frequent patterns. They may simply indicate variations in the examinations prescribed due to specific patient conditions, otherwise they may reveal limitation in accessing public health services or identify errors in the data entry process. In all these cases, the detection of the anomalies is useful for a successive analysis by domain experts. We applied our method to three case studies to show how it can be successfully exploited in real medical domain.

A Review: Data mining over Multi-Relations

In this paper, Multi-relational data mining enables pattern mining from multiple tables. Multi-relational data mining algorithms can be used as practical proposal to overcome the deficiency of conventional algorithms. Multi-relational data mining algorithms directly extract frequent patterns from different registers in efficient manner without need of transfer the data in a single table will, on the other hand, used the available memory space is not enough to ensure the production of large amounts of data. For this reason, and the use of space, algorithms are an integral care for the prospection of large repositories. The paper provides the overview of multi relation data mining techniques and classification algorithms. It also defines the frequent pattern mining. The presented paper discussed the various architecture and issues related to multi table data mining. A lot of literature has been proposed in this area. Some of them has discussed in this paper.

Extraction of Frequent Patterns from Web Logs using Web Log Mining Techniques

World Wide Web is a huge repository of web pages and links. It provides profusion of information for the Internet users. The growth of web is tremendous as approximately one million pages are added daily. User’s accesses are recorded in web logs. Because of the incredible usage of web, the web log files are growing at a faster rate and the size is becoming huge. Web data mining is the application of data mining techniques in web data. Web log Mining applies mining techniques in log data to extract the behaviour of users. Web log mining consists of three phases pre-processing, pattern discovery and pattern analysis. Web log data is usually noisy and ambiguous and pre-processing is an important process before web log mining. For discovering patterns sessions are to be constructed efficiently. This paper presents the existing work done to extracting patterns by using decision tree methodology in the technique of web log mining.

Analyzing consumers’ shopping behavior using RFID data and pattern mining

The development of sensor networks has enabled detailed tracking of customer behavior in stores. Shopping path data which records each customer’s position and time information is attracting attention as new marketing data. However, there are no proposed marketing models which can identify good customers from huge amounts of time series data on customer movement in the store. This research aims to use shopping path data resulting from tracking customer behavior in the store, using information on the sequence of visiting each product zone in the store and staying time at each product zone, to find how they affect purchasing. To discover useful knowledge for store management, shopping paths data has been transformed into sequence data including information on visit sequence and staying times in the store, and LCMseq has been applied to them to extract frequent sequence patterns. In this paper, we find characteristic in-store behavior patterns of good customers by using actual data of a Japanese supermarket.

An Efficient Parallel Algorithm for Mining Frequent Pattern

Extraction of frequent patterns in transaction-oriented database is crucial to several data mining tasks such as association rule generation, time series analysis, classification, etc. An Efficient Parallel algorithm for Mining frequent pattern (EPM) was proposed and Fast Distributed association rules Mining (FDM) algorithm was improved. Hash table technology was used to improve the generation efficiency of the 2nd candidate items . It also reduces the number of transactions in transaction database using Tid table technology. A master-slave model of parallel algorithm for mining association rules is designed in the algorithm to reduce the communication cost. The experimental results show that this algorithm has a high efficiency to deal with large database.

Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings

Discovering semantic knowledge is significant for understanding and interpreting how people interact in a meeting discussion. In this paper, we propose a mining method to extract frequent patterns of human interaction based on the captured content of face-to-face meetings. Human interactions, such as proposing an idea, giving comments, and expressing a positive opinion, indicate user intention toward a topic or role in a discussion. Human interaction flow in a discussion session is represented as a tree. Tree-based interaction mining algorithms are designed to analyze the structures of the trees and to extract interaction flow patterns. The experimental results show that we can successfully extract several interesting patterns that 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 interactions.

BioDARA: Data Summarization Approach to Extracting Bio-Medical Structuring Information

Problem statement: Due to the ever growing amount of biomedical datasets stored in multiple tables, Information Extraction (IE) from these datasets is increasingly recognized as one of the crucial technologies in bioinformatics. However, for IE to be practically applicable, adaptability of a system is crucial, considering extremely diverse demands in biomedical IE application. One should be able to extract a set of hidden patterns from these biomedical datasets at low cost. Approach: In this study, a new method is proposed, called Bio-medical Data Aggregation for Relational Attributes (BioDARA), for automatic structuring information extraction for biomedical datasets. BioDARA summarizes biomedical data stored in multiple tables in order to facilitate data modeling efforts in a multi-relational setting. BioDARA has the advantages or capabilities to transform biomedical data stored in multiple tables or databases into a Vector Space model, summarize biomedical data using the Information Retrieval theory and finally extract frequent patterns that describe the characteristics of these biomedical datasets. Results: the results show that data summarization performed by DARA, can be beneficial in summarizing biomedical datasets in a complex multi-relational environment, in which biomedical datasets are stored in a multi-level of one-to-many relationships and also in the case of datasets stored in more than one one-to-many relationships with non-target tables. Conclusion: This study concludes that data summarization performed by BioDARA, can be beneficial in summarizing biomedical datasets in a complex multi-relational environment, in which biomedical datasets are stored in a multi-level of one-to-many relationships.

Unsupervised Spatiotemporal Mining of Satellite Image Time Series Using Grouped Frequent Sequential Patterns

An important aspect of satellite image time series is the simultaneous access to spatial and temporal information. Various tools allow end users to interpret these data without having to browse the whole data set. In this paper, we intend to extract, in an unsupervised way, temporal evolutions at the pixel level and select those covering at least a minimum surface and having a high connectivity measure. To manage the huge amount of data and the large number of potential temporal evolutions, a new approach based on data-mining techniques is presented. We have developed a frequent sequential pattern extraction method adapted to that spatiotemporal context. A successful application to crop monitoring involving optical data is described. Another application to crustal deformation monitoring using synthetic aperture radar images gives an indication about the generic nature of the proposed approach.

Mining Spatio-Temporal Patterns in Trajectory Data

Spatio-temporal patterns extracted from historical trajectories of moving objects reveal important knowledge about movement behavior for high quality LBS services. Existing approaches transform trajectories into sequences of location symbols and derive frequent subsequences by applying conventional sequential pattern mining algorithms. However, spatio-temporal correlations may be lost due to the inappropriate approximations of spatial and temporal properties. In this paper, we address the problem of mining spatio-temporal patterns from trajectory data. The inefficient description of temporal information decreases the mining efficiency and the interpretability of the patterns. We provide a formal statement of efficient representation of spatio-temporal movements and propose a new approach to discover spatio-temporal patterns in trajectory data. The proposed method first finds meaningful spatio-temporal regions and extracts frequent spatio-temporal patterns based on a prefix-projection approach from the sequences of these regions. We experimentally analyze that the proposed method improves mining performance and derives more intuitive patterns.