Frequent Sequence Mining Research Articles

Frequent Attack Sequences-based Network log Mining

As an important part of modern IT infrastructure, network provides great convenience for people to exchange information and share resources. However, network still faces with many threats such as network virus, hacker attacks, data theft and tampering and so on. Network logs includes a lot of valuable information about all behaviours happened in the network. How to analyse these network log to enhance the security of network becomes consequently the focus of many researchers. In this paper, we first design three similarity functions between two network attack records to create the network attack sequences, and then present a PrefixSpan-based frequent attack sequence mining algorithm to identify all frequent attack sequences in network log. The experimental results show that the PrefixSpan-based frequent attack sequence mining algorithm has shorter executing time and less running space than the Apriori algorithm. The PrefixSpan-based frequent attack sequence mining algorithm provides a network log analysing method for intrusion detection.

A Novel Selection and Matching of Patterns Model for Traffic Prediction in Wireless Network

Data mining techniques are becoming increasing popular and important in the big data era. Among them, frequent patterns mining technique has matured, with this technique, we can obtain the association rules to predict the data in cellular network. Existing research mainly focus on the frequent item sets mining, rarely involves in the time series and FPM algorithm usually used user-defined minimum support in various training datasets, which is rigorous, may influence the number of the frequent patterns and fail to select the reasonable patterns. Moreover, traditional association rules mining only relies on single evaluation criterion such as confidence or support, which leads to select unsound rules. This paper introduces an optimized frequent pattern mining algorithm, improving the setting of support in the process of mining frequent sequences and presenting a new evaluation criterion for the candidate patterns. Experiments were conducted to select appropriate parameters, support and evaluation criterion. Furthermore, we apply the above conclusion to the cellular flow data prediction, and compare the runtime, matching rate, RMSE and MAPE of proposed algorithms with those of improved Markov algorithm to examine the effectiveness of algorithm.

Approach to discovering companion patterns based on traffic data stream

A companion of moving objects is an object group that move together in a period of time. Platoon companions are a generalised companion pattern, which describes a group of objects that move together for time segments, each with some minimum consecutive duration of time. This study proposes a method that can instantly discover platoon companions from a special kind of streaming traffic data, called automatic number plate recognition data. Compared to related approaches, the authors transform the companion discovery into a frequent sequence mining problem. The authors propose a data structure, platoon tree (PTree), to record discovered platoon companions. To reduce the cost of tree traversal during mining platoon companions, they utilise the last two together-moving objects of a group to update PTree. Finally, a lot of experiments have been carried out to show the efficiency and effectiveness of the proposed approach.

An Efficient Algorithm for Mining Maximal Frequent Sequential Patterns in Large Databases

Frequent sequence mining is one of the important research directions of click stream analysis, this paper studies the problem of mining maximal frequent sequences in mobile app clickstreams. Different from frequent itemsets mining, frequent sequence mining takes the time order of elements into account. In this paper, MFSGrowth(Maximal Frequent Sequence Growth) is proposed for fast discovery of frequent sequence. MFSGrowth is an efficient algorithm based on the FP Tree, combined with the storage characteristics of the TriedTree, experiments show that the algorithm performs well in both mining time and storage efficiency.

Prediction of Broadband Spectrum Utilization Data for Spectrum Sharing

The 5th generation mobile network system (5G) comprises the infrastructure of next generation communication services such as large-capacity video delivery services and real time entertainment services. It is extremely important to secure more spectrum resources to satisfy the 5G performance requirements. Solutions include spectrum sharing technology. This paper shows that the sequences consisting of the channel vacancy durations follow the exponential distribution by using the spectrum utilization data in an urban area where the spectrum is used at high density. The spectrum usage state could be described by using the channel vacancy periods. Furthermore, a prediction method is proposed of the future channel vacancy period with the frequent sequence mining technology by using short time period observation data. The result of the evaluation experiment is also reported.

Data mining in distributed environment: a survey

Due to the rapid growth of resource sharing, distributed systems are developed, which can be used to utilize the computations. Data mining (DM) provides powerful techniques for finding meaningful and useful information from a very large amount of data, and has a wide range of real‐world applications. However, traditional DM algorithms assume that the data is centrally collected, memory‐resident, and static. It is challenging to manage the large‐scale data and process them with very limited resources. For example, large amounts of data are quickly produced and stored at multiple locations. It becomes increasingly expensive to centralize them in a single place. Moreover, traditional DM algorithms generally have some problems and challenges, such as memory limits, low processing ability, and inadequate hard disk, and so on. To solve the above problems, DM on distributed computing environment [also called distributed data mining (DDM)] has been emerging as a valuable alternative in many applications. In this study, a survey of state‐of‐the‐art DDM techniques is provided, including distributed frequent itemset mining, distributed frequent sequence mining, distributed frequent graph mining, distributed clustering, and privacy preserving of distributed data mining. We finally summarize the opportunities of data mining tasks in distributed environment. WIREs Data Mining Knowl Discov 2017, 7:e1216. doi: 10.1002/widm.1216This article is categorized under: Application Areas > Business and Industry Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Technologies > Computer Architectures for Data Mining

Mining Time-constrained Sequential Patterns with Constraint Programming

Constraint Programming (CP) has proven to be an effective platform for constraint based sequence mining. Previous work has focused on standard frequent sequence mining, as well as frequent sequence mining with a maximum ’gap’ between two matching events in a sequence. The main challenge in the latter is that this constraint can not be imposed independently of the omnipresent frequency constraint. Indeed, the gap constraint changes whether a subsequence is included in a sequence, and hence its frequency. In this work, we go beyond that and investigate the integration of timed events and constraining the minimum/maximum gap as well as minimum/maximum span. The latter constrains the allowed time between the first and last matching event of a pattern. We show how the three are interrelated, and what the required changes to the frequency constraint are. Key in our approach is the concept of an extension window defined by gap/span and we develop techniques to avoid scanning the sequences needlessly, as well as using a backtracking-aware data structure. Experiments demonstrate that the proposed approach outperforms both specialized and CP-based approaches in almost all cases and that the advantage increases as the minimum frequency threshold decreases. This paper is an extension of the original manuscript presented at CPAIOR’17 [5].

FCloSM, FGenSM: two efficient algorithms for mining frequent closed and generator sequences using the local pruning strategy

Mining frequent sequences in sequential databases are highly valuable for many real-life applications. However, in several cases, especially when databases are huge and when low minimum support thresholds are used, the cardinality of the result set can be enormous. Consequently, algorithms for discovering frequent sequences exhibit poor performance, showing an important increase in execution time, memory consumption and storage space usage. To address this issue, researchers have studied the tasks of mining frequent closed and generator sequences, as they provide several benefits when compared to the set of frequent sequences. One of the most important benefits is that the cardinalities of frequent closed and generator sequences are generally much less than the cardinality of frequent sequences. Hence, humans find it more convenient to analyze the information provided by closed and generator sequences. Moreover, it was shown that frequent closed sequences have the advantage of being lossless, and they thus preserve information about the frequency of all frequent subsequences, while generator sequences can provide higher accuracy for sequence classification tasks since they are the smallest patterns that characterize groups of sequences. Besides, frequent closed sequences can be combined with generators to produce non-redundant sequential rules and recover the complete set of frequent sequences and their frequencies. This paper proposes two novel algorithms named FCloSM and FGenSM to mine frequent closed and generator sequences efficiently. These algorithms are based on new pruning conditions called extended early elimination (3E) and early pruning techniques named EPCLO and EPGEN, designed to identify non-closed and non-generator patterns early. Based on these techniques, two local pruning strategies called LPCLO and LPGEN are proposed to eliminate non-closed and non-generator patterns more efficiently at two successive levels of the prefix search tree without performing subsequence relation checking. These theoretical results, which are the basis of FCloSM and FGenSM, are mathematically proved and are shown to be more general than those presented in previous work. Extensive experiments show that FCloSM and FGenSM are one to two orders of magnitude faster than the state-of-the-art algorithms for discovering frequent closed sequences (CloSpan, BIDE, ClaSP and CM-ClaSP) and for mining frequent generators (FEAT, FSGP and VGEN), and that FCloSM and FGenSM consume much less memory.

A cognition graph approach for insights generation from event sequences

In recent years, cognition map techniques for human insights have already played a significant part in complex or ill-structured problem solving. There are increasing interests on computational methods rather than hand-drawing methods to build an cognition graph for insights generation. In this paper, a systematic approach called Temporal-IdeaGraph is proposed to build a directed cognition graph based on event sequences. Firstly, an algorithm of frequent sequence mining is employed to capture sequential patterns and a method is then designed to remove duplicate patterns. Secondly, relevant patterns are merged and visualized into a directed cognition graph. An algorithm is further proposed to identify bridge events and bridge patterns which would trigger human's deeper insights for better decision making. Finally, two real case studies validate the effectiveness of proposed approach.

Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse

ABSTRACTAs collaborative learning is actualized through evolving dialogues, temporality inevitably matters for the analysis of collaborative learning. This study attempts to uncover sequential patterns that distinguish “productive” threads of knowledge-building discourse. A database of Grade 1–6 knowledge-building discourse was first coded for the posts’ contribution types and discussion threads’ productivity. Two distinctive temporal analysis techniques – Lag-sequential Analysis (LsA) and Frequent Sequence Mining (FSM) – were subsequently applied to detecting sequential patterns among contribution types that distinguish productive threads. The findings of LsA indicated that threads that were characterized by mere opinion-giving did not achieve much progress, while threads having more transitions among questioning, obtaining information, working with information, and theorizing were more productive. FSM further uncovered from productive threads distinguishing frequent sequences involving sustained theorizing, integrated use of evidence, and problematization of proposed theories. Based on the significance of studying temporality in collaborative learning revealed in the study, we advocate for more analytics tapping into temporality of learning.

Radio Wave Environment Analysis at Different Locations Based on Frequent Pattern Mining

IoT devices using radio wave functions continue to significantly increase. Since the frequency resources of radio waves are insufficient, acquiring them is a big problem for 5th generation wireless communication systems. Frequency sharing with radio systems is one of critical techniques to get more frequency resources. Data mining technologies are often used to determine the overall tendencies from some sample data. Frequent sequence mining technology is sometimes used to get the characteristics by a series of data. This study analyzed the characteristics of the radio environments of the ground and the sky in suburban areas using frequent sequence mining technology and identified the differences in the appearance of the sequences of every place and frequency. This study also observed the effects of radio wave fading in a series of high frequent sequences.

FDSMO: Frequent DNA Sequence Mining Using FBSB and Optimization

DNA Sequence mining helps in discovering the patterns which can occur frequently, structures of DNA in DNA data sets. Frequent pattern mining is a central strategy for affiliation guideline discovery, but existing calculations experience the ill effects of low effectiveness or poor error rate on the grounds that natural groupings vary from general successions with more attributes. In our last work, we proposed Prefix Span with Group Search Optimization (PSGSO) to optimize the mined results from the Prefix Span method. We propose a new method called Frequent DNA Sequence Mining using Optimization (FDSMO) which combines Frequent Biological Sequence based on Bitmap (FBSB) and Hybrid of Firefly and Group Search Optimization (HFGSO) in this paper. The FDSMO process includes three stages: (i) applying the Frequent Biological Sequence based on Bitmap (ii) calculate length, width and regular expression (iii) optimization using HFGSO. The exploratory results demonstrate that FDSMO performs better than the existing methods, both in terms of running time and scalability.

Differentially Private Frequent Sequence Mining.

In this paper, we study the problem of mining frequent sequences under the rigorous differential privacy model. We explore the possibility of designing a differentially private frequent sequence mining (FSM) algorithm which can achieve both high data utility and a high degree of privacy. We found, in differentially private FSM, the amount of required noise is proportionate to the number of candidate sequences. If we could effectively prune those unpromising candidate sequences, the utility and privacy tradeoff can be significantly improved. To this end, by leveraging a sampling-based candidate pruning technique, we propose PFS2, a novel differentially private FSM algorithm. It is the first algorithm that supports the general gap-constrained FSM in the context of differential privacy. The gap constraints in FSM can be used to limit the mining results to a controlled set of frequent sequences. In our PFS2 algorithm, the core is to utilize sample databases to prune the candidate sequences generated based on the downward closure property. In particular, we use the noisy local support of candidate sequences in the sample databases to estimate which candidate sequences are potentially frequent. To improve the accuracy of such private estimations, a gap-aware sequence shrinking method is proposed to enforce the length constraint on the sample databases. Moreover, to calibrate the amount of noise required by differential privacy, a gap-aware sensitivity computation method is proposed to obtain the sensitivity of the local support computations with different gap constraints. Furthermore, to decrease the probability of misestimating frequent sequences as infrequent, a threshold relaxation method is proposed to relax the user-specified threshold for the sample databases. Through formal privacy analysis, we show that our PFS2 algorithm is ϵ-differentially private. Extensive experiments on real datasets illustrate that our PFS2 algorithm can privately find frequent sequences with high accuracy.

API usage pattern recommendation for software development

We propose an approach to improve the mining of less frequent API usage patterns.The approach can recommend API usage patterns with high completeness and low redundancy.An improvement of 18% on F-measure comparing to a frequent itemset mining based approach is observed. Application Programming Interfaces (APIs) facilitate pragmatic reuse and improve the productivity of software development. An API usage pattern documents a set of method calls from multiple API classes to achieve a reusable functionality. Existing approaches often use frequent-sequence mining to extract API usage patterns. However, as reported by earlier studies, frequent-sequence mining may not produce a complete set of usage patterns. In this paper, we explore the possibility of mining API usage patterns without relying on frequent-pattern mining. Our approach represents the source code as a network of object usages where an object usage is a set of method calls invoked on a single API class. We automatically extract usage patterns by clustering the data based on the co-existence relations between object usages. We conduct an empirical study using a corpus of 11,510 Android applications. The results demonstrate that our approach can effectively mine API usage patterns with high completeness and low redundancy. We observe 18% and 38% improvement on F-measure and response time respectively comparing to usage pattern extraction using frequent-sequence mining.

Probabilistic Static Load-Balancing of Parallel Mining of Frequent Sequences

Frequent sequence mining is well known and well studied problem in datamining. The output of the algorithm is used in many other areas like bioinformatics, chemistry, and market basket analysis. Unfortunately, the frequent sequence mining is computationally quite expensive. In this paper, we present a novel parallel algorithm for mining of frequent sequences based on a static load-balancing. The static load-balancing is done by measuring the computational time using a probabilistic algorithm. For reasonable size of instance, the algorithms achieve speedups up to $\approx 3/4\cdot P$ where $P$ is the number of processors. In the experimental evaluation, we show that our method performs significantly better then the current state-of-the-art methods. The presented approach is very universal: it can be used for static load-balancing of other pattern mining algorithms such as itemset/tree/graph mining algorithms.

Mining non-redundant sequential rules with dynamic bit vectors and pruning techniques

Most algorithms for mining sequential rules focus on generating all sequential rules. These algorithms produce an enormous number of redundant rules, making mining inefficient in intelligent systems. In order to solve this problem, the mining of non-redundant sequential rules was recently introduced. Most algorithms for mining such rules depend on patterns obtained from existing frequent sequence mining algorithms. Several steps are required to organize the data structure of these sequences before rules can be generated. This process requires a great deal of time and memory. The present study proposes a technique for mining non-redundant sequential rules directly from sequence databases. The proposed method uses a dynamic bit vector data structure and adopts a prefix tree in the mining process. In addition, some pruning techniques are used to remove unpromising candidates early in the mining process. Experimental results show the efficiency of the algorithm in terms of runtime and memory usage.

Mining frequent biological sequences based on bitmap without candidate sequence generation

Biological sequences carry a lot of important genetic information of organisms. Furthermore, there is an inheritance law related to protein function and structure which is useful for applications such as disease prediction. Frequent sequence mining is a core technique for association rule discovery, but existing algorithms suffer from low efficiency or poor error rate because biological sequences differ from general sequences with more characteristics. In this paper, an algorithm for mining Frequent Biological Sequence based on Bitmap, FBSB, is proposed. FBSB uses bitmaps as the simple data structure and transforms each row into a quicksort list QS-list for sequence growth. For the continuity and accuracy requirement of biological sequence mining, tested sequences used during the mining process of FBSB are real ones instead of generated candidates, and all the frequent sequences can be mined without any errors. Comparing with other algorithms, the experimental results show that FBSB can achieve a better performance on both run time and scalability.

Discovery of bidirectional contiguous column coherent bicluster in time-series gene expression data

The application of high-throughput microarray has led to massive gene expression data, urging effective methodology for analysis. Biclustering comes out and serves as a useful tool, performing simultaneous clustering on rows and columns to find subsets of coherently expressed genes and conditions. Specially, in analysis of time–series gene expression data, it is meaningful to restrict biclusters to contiguous time points concerning coherent evolutions. In this paper, BCCC-Bicluster is proposed as an extension of CCC-Bicluster. An exact algorithm based on frequent sequential mining is proposed to find all maximal BCCC-Biclusters. The newly defined Frequent-Infrequent Tree-Array (FITA) is constructed to speed up the traversal process, with useful strategies originating from Apriori property to avoid redundant work. To make it more efficient, the bitwise operation XOR is applied to capture identical or opposite contiguous patterns between two rows. The algorithm is tested in simulated data, yeast microarray data and human microarray data. The experimental results show the proposed algorithm had better performance on the ability to recover the planted biclusters in the synthetic data than CCC-Biclusters and outperformed the one without FITA in speed and scalability. In the enrichment analysis, BCCC-Biclusters are proven to find more significant GO terms involved in biological processes than other three kinds of up-to-date biclusters.

Differentially private maximal frequent sequence mining

In this paper, we study the problem of designing a differentially private algorithm for mining maximal frequent sequences, which can not only achieve high data utility and a high degree of privacy, but also provide high time efficiency. To solve this problem, we present a new differentially private algorithm, which is referred to as DP-MFSM. DP-MFSM consists of three phases: pre-processing phase, expected frequent sequence mining (ESM) phase, and candidate extraction and verification (CEV) phase. Specifically, in the pre-processing phase, we first extract some statistical information from the input database, and use the extracted information to determine the values of some variables which will be used in the ESM phase. Then, in the ESM phase, we randomly partition the input database into several sub-databases, and use a partition-based ESM technique to find expected frequent sequences, which are a subset of candidate frequent sequences and more likely to be frequent. At last, in the CEV phase, we extract candidate maximal frequent sequences from the discovered expected frequent sequences, and use a splitting-based technique to verify which candidates are actually frequent in the input database. Through privacy analysis, we show that our DP-MFSM algorithm is ε-differentially private. Extensive experiments on real-world datasets illustrate that our DP-MFSM algorithm can substantially outperform alternative approaches.

Mining and exploring care pathways from electronic medical records with visual analytics

ObjectiveIn order to derive data-driven insights, we develop Care Pathway Explorer, a system that mines and visualizes a set of frequent event sequences from patient EMR data. The goal is to utilize historical EMR data to extract common sequences of medical events such as diagnoses and treatments, and investigate how these sequences correlate with patient outcome. Materials and methodsThe Care Pathway Explorer uses a frequent sequence mining algorithm adapted to handle the real-world properties of EMR data, including techniques for handling event concurrency, multiple levels-of-detail, temporal context, and outcome. The mined patterns are then visualized in an interactive user interface consisting of novel overview and flow visualizations. ResultsWe use the proposed system to analyze the diagnoses and treatments of a cohort of hyperlipidemic patients with hypertension and diabetes pre-conditions, and demonstrate the clinical relevance of patterns mined from EMR data. The patterns that were identified corresponded to clinical and published knowledge, some of it unknown to the physician at the time of discovery. ConclusionCare Pathway Explorer, which combines frequent sequence mining techniques with advanced visualizations supports the integration of data-driven insights into care pathway discovery.