Low Support Threshold Research Articles

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.

A Novel Approach for Mining Closed Clickstream Patterns

Closed sequential pattern (CSP) mining is an optimization technique in sequential pattern mining because they produce more compact representations. Additionally, the runtime and memory usage required for mining CSPs is much lower than the sequential pattern mining. This task has fascinated numerous researchers. In this study, we propose a novel approach for closed clickstream pattern mining using C-List (CCPC) data structure. Closed clickstream pattern mining is a more specific task of CSP mining that has been lacking in research investment; nevertheless, it has promising applications in various fields. CCPC consists of two key steps: It initially builds the SPPC-tree and the C-List for each frequent 1-pattern and then determines all frequently closed clickstream 1-patterns; next, it constructs the C-List for each frequent k-pattern and mines the remaining frequently closed k-patterns. The proposed method is optimized by modifying the SPPC-tree structure and a new property is added into each node element in both the SPPC-tree and C-Lists to quickly prune nonclosed clickstream. Experimental results conducted on several datasets show that the proposed method is better than the previous techniques and improves the runtime and memory usage in most cases, especially when using low minimum support thresholds on the huge databases.

An Approach for Seasonally Periodic Frequent Pattern Mining in Retail Supermarket

Periodic-Frequent pattern mining is an empirical data mining technique that is used to investigate the customer purchase behavior pattern in the retail market. It provides the most extreme advantages to a retailer for product placements, product segmentation, store layout, selling strategies and different merchandising decisions to improve customer satisfaction as well as increase the sales. Traditionally, the single minimum support based frequent pattern mining approaches missed many lower supported items for high support threshold value, and on the other sites, the number of frequent patterns explode for low support threshold value. The retail market is highly seasonal, a number of items those are not periodic-frequent at all but those are periodic and frequent at a specific season, as results a huge number of seasonal periodic-frequent itemsets are remains out of reach. To resolve this problem, we propose an approach for extracting seasonally periodic-frequent patterns with user specified minimum support and maximum periodicity thresholds on multilevel retail supermarket dataset in this paper. The dataset has been created by collecting transaction slips in a Supermarket of Bangladesh. The results of our proposed approach extract many hidden seasonal patterns in transactional database for easy merchandising decisions and it shows the superiority over the traditional systems.

EFFICIENTLY MINING CLOSED SEQUENTIAL PATTERNS USING PREFIX TREE

Mining closed sequential patterns is one of important tasks in data mining. It is proposed to resolve difficult problems in mining sequential pattern such as 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. So, using the parent–child relationship on prefix tree structure to improve the performance of the mining sequential patterns process from the sequence database is also one of important methods in data mining, specially in the mining closed sequential patterns. This paper produces an effective algorithm and experimental results for mining closed sequential patterns from the sequence database using the parent–child relationship on the prefix tree structure. Experimental results show that the performance runtime of the proposed algorithm is much faster than that of other algorithms by more than one order of magnitude and the number of sequential patterns is reduced significantly.

An Efficient Method for Mining Top-K Closed Sequential Patterns

The problem of exploiting Closed Sequential Patterns (CSPs) is an essential task in data mining, with many different applications. It is used to resolve the situations of huge databases or low minimum support (minsup) thresholds in mining sequential patterns. However, it is challenging and needs a lot of time to customize the minsup values for generating appropriate numbers of CSPs desired by users. To conquer this issue, the TSP algorithm for mining top-k CSPs was previously proposed, with k being a given parameter. The algorithm would return the k CSPs which have the highest support values in a database. However, its execution time and memory usage were high. In this paper, an algorithm named TKCS (Top-K Closed Sequences) is proposed to mine the top-k CSPs efficiently. To improve the execution time and memory usage, it uses a vertical bitmap database to represent data. Besides, it adopts some useful strategies in the process of exploiting the top-k CSPs such as: always choosing the sequential patterns with the greatest support values for generating candidate patterns and storing top-k CSPs in an ascending order of the support values to increase the minsup value more quickly. The empirical results show that TKCS has better performance than TSP for discovering the top-k CSPs in terms of both runtime and memory usage.

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.

The union default solution to declining union membership

The continued stasis in the now much reduced aggregate levels of union membership in Britain and elsewhere has such portent for widening inequalities in wealth and power under the neo-liberal variant of late capitalism that a union default requires introduction. In all countries around the world, the de facto system is a non-union default. Under a union default, all workers would be defaulted into membership of the appropriate union. This is not a proposal for compulsory membership, a contravention of individual liberty nor the return of the closed shop by the backdoor because workers will have the right to opt out of membership. The right to be the appropriate default union to which workers would be defaulted into in any workplace would be gained by the union passing a low minimum support threshold. This would bring with it the right to bargain over pay and conditions.

CmFSM: a scalable CPU-MIC coordinated drug-finding tool by frequent subgraph mining

BackgroundFrequent subgraphs mining is a significant problem in many practical domains. The solution of this kind of problem can particularly used in some large-scale drug molecular or biological libraries to help us find drugs or core biological structures rapidly and predict toxicity of some unknown compounds. The main challenge is its efficiency, as (i) it is computationally intensive to test for graph isomorphisms, and (ii) the graph collection to be mined and mining results can be very large. Existing solutions often require days to derive mining results from biological networks even with relative low support threshold. Also, the whole mining results always cannot be stored in single node memory.ResultsIn this paper, we implement a parallel acceleration tool for classical frequent subgraph mining algorithm called cmFSM. The core idea is to employ parallel techniques to parallelize extension tasks, so as to reduce computation time. On the other hand, we employ multi-node strategy to solve the problem of memory constraints. The parallel optimization of cmFSM is carried out on three different levels, including the fine-grained OpenMP parallelization on single node, multi-node multi-process parallel acceleration and CPU-MIC collaborated parallel optimization.ConclusionsEvaluation results show that cmFSM clearly outperforms the existing state-of-the-art miners even if we only hold a few parallel computing resources. It means that cmFSM provides a practical solution to frequent subgraph mining problem with huge number of mining results. Specifically, our solution is up to one order of magnitude faster than the best CPU-based approach on single node and presents a promising scalability of massive mining tasks in multi-node scenario. More source code are available at:Source Code: https://github.com/ysycloud/cmFSM.

Most Any Reason Is Better Than None: Consequences of Implausible Reasons and Warrants in Brief Written Arguments

Argumentation schema theory guided four experiments on the processing of plausible and implausible reasons and warrant statements testing the hypothesis that most reasons produce greater agreement with claims than when claims are presented without support. Another hypothesis was that leaving warrants unstated often produces greater agreement than when the warrant is made explicit. In Study 1, American participants were more likely to agree with claims after they read arguments than beforehand—even those with implausible reasons and warrants. In Study 2, American history and environmental science majors read brief arguments and agreed more with implausible arguments than claims alone. Study 3, with Chinese participants, replicated some but not all earlier results. In Study 4, with Chinese participants, blatantly false claims supported by bogus reasons yielded marginally greater agreement than unsupported claims. These findings suggest that many people have uncritical argumentation schemata with low support thresholds, making them vulnerable to weak and bogus arguments.

APPLYING THE ATTRIBUTED PREFIX TREE FOR MINING CLOSED SEQUENTIAL PATTERNS

Mining closed sequential patterns is one of important tasks in data mining. It is proposed to resolve difficult problems in mining sequential pattern such as 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. This paper applies the characteristics of closed sequential patterns and sequence extensions into the prefix tree structure to mine closed sequential patterns from the sequence database. The paper uses the parent–child relationship on prefix tree structure and each node on prefix tree is also added fields to determine whether that is a closed sequential pattern or not. Experimental results show that the number of sequential patterns is reduced significantly.

A Parallel Approach for Frequent Subgraph Mining in a Single Large Graph Using Spark

Frequent subgraph mining (FSM) plays an important role in graph mining, attracting a great deal of attention in many areas, such as bioinformatics, web data mining and social networks. In this paper, we propose SSiGraM (Spark based Single Graph Mining), a Spark based parallel frequent subgraph mining algorithm in a single large graph. Aiming to approach the two computational challenges of FSM, we conduct the subgraph extension and support evaluation parallel across all the distributed cluster worker nodes. In addition, we also employ a heuristic search strategy and three novel optimizations: load balancing, pre-search pruning and top-down pruning in the support evaluation process, which significantly improve the performance. Extensive experiments with four different real-world datasets demonstrate that the proposed algorithm outperforms the existing GraMi (Graph Mining) algorithm by an order of magnitude for all datasets and can work with a lower support threshold.

An Efficient Itemset Representation for Mining Frequent Patterns in Transactional Databases

In this paper we propose very efficient itemset representation for frequent itemset mining from transactional databases. The combinatorial number system is used to uniquely represent frequent k-itemset with just one integer value, for any k ≥ 2. Experiments show that memory requirements can be reduced up to 300 %, especially for very low minimal support thresholds. Further, we exploit combinatorial number schema for representing candidate itemsets during iterative join-based approach. The novel algorithm maintains one-dimensional array rank, starting from k = 2nd iteration. At the index r of the array, the proposed algorithm stores unique integer representation of the r-th candidate in lexicographic order. The rank array provides joining of two candidate k-itemsets to be O(1) instead of O(k) operation. Additionally, the rank array provides faster determination which candidates are contained in the given transaction during the support count and test phase. Finally, we believe that itemset ranking by combinatorial number system can be effectively integrated into pattern-growth algorithms, that are state-of-the-art in frequent itemset mining, and additionally improve their performances.

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.

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.

Reference itemsets: useful itemsets to approximate the representation of frequent itemsets

Deriving frequent itemsets from databases is an important research issue in data mining. The number of frequent itemsets may be unusually large when a low minimum support threshold is given. As such, the design of a compact representation to compress and describe them is an interesting topic. In the past, most related research on compact representation focused on frequent closed itemsets and frequent maximal itemsets. The former is a lossless compact technology that can totally recover all frequent itemsets and their frequencies. Contrarily, the latter may lose some information regarding frequent itemsets, because it reserves frequent itemsets only and is unable to identify their frequency. In this paper, we propose a new compact representation that lies between closed itemsets and maximal itemsets. It can reserve all frequent itemsets and identify their approximate frequency. In addition, an efficient algorithm that corresponds to this new concept is designed to find related key information in databases. Finally, a series of experiments are conducted to show the effectiveness of compact representation and the performance of the proposed algorithm.

An approximate representation of hypercliques

A hyperclique is an itemset containing items that are strongly correlated with each other, based on a user-specified threshold. Hypercliques (HC) have been successfully used in a number of applications, for example clustering and noise removal. Even though the HC collection has been shown to respond well to datasets with skewed support distribution and low support threshold, it may still grow very large for dense datasets and lower h-confidence threshold. Recently, we proposed a condensed representation of HC, the Non-Derivable Hypercliques (NDHC). NDHC was shown to have substantial advantages over HC, especially for dense datasets and lower h-confidence values. In this paper, we propose an approximation of the NDHC collection, called Approximate Non-Derivable Hypercliques (ANDHC). This collection is a subset of NDHC and is generated based on a user-input error. We present an efficient algorithm to mine all ANDHC sets, and an algorithm to approximately derive the HC collection from the ANDHC collection. Through experiments with several datasets and parameter combinations, the ANDHC collection is shown to have significant collection size reduction from NDHC even for small error values. It is also shown that based on ANDHC we can generate a good approximation of the entire HC collection. Finally, we experiment with applying our proposed collections to enhance clustering with encouraging results.

CCSpan: Mining closed contiguous sequential patterns

Existing closed sequential pattern mining generates a more compact yet complete resulting set compared with general sequential pattern mining. However, conventional closed sequential pattern mining algorithms pose a great challenge at spawning a large number of inefficient and redundant patterns, especially when using low support thresholds or pattern-enriched databases. Driven by wide applications of sequential patterns with contiguous constraint, we propose CCSpan (Closed Contiguous Sequential pattern mining), an efficient algorithm for mining closed contiguous sequential patterns, which contributes to a much more compact pattern set but with the same information w.r.t. closed sequential patterns. Moreover, with the shorter feature of patterns, the closed contiguous sequential patterns are preferred for feature selection and sequence classification based on the Minimum Description Length principle. CCSpan adopts a novel snippet-growth paradigm to generate a series of snippets as candidates, each of which is attached with a set of item(s) that precisely record the pattern’s occurrences in the database, and CCSpan leverages three pruning techniques to improve the computational efficiency significantly. Our experiments based on both sparse and dense datasets demonstrated that CCSpan is efficient and scalable in terms of both database size and support threshold.

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...

Parallel Associative Classification Data Mining Frameworks Based MapReduce

Associative classification (AC) is a research topic that integrates association rules with classification in data mining to build classifiers. After dissemination of the Classification-based Association Rule algorithm (CBA), the majority of its successors have been developed to improve either CBA's prediction accuracy or the search for frequent ruleitems in the rule discovery step. Both of these steps require high demands in processing time and memory especially in cases of large training data sets or a low minimum support threshold value. In this paper, we overcome the problem of mining large training data sets by proposing a new learning method that repeatedly transforms data between line and item spaces to quickly discover frequent ruleitems, generate rules, subsequently rank and prune rules. This new learning method has been implemented in a parallel Map-Reduce (MR) algorithm called MRMCAR which can be considered the first parallel AC algorithm in the literature. The new learning method can be utilised in the different steps within any AC or association rule mining algorithms which scales well if contrasted with current horizontal or vertical methods. Two versions of the learning method (Weka, Hadoop) have been implemented and a number of experiments against different data sets have been conducted. The ground bases of the comparisons are classification accuracy and time required by the algorithm for data initialization, frequent ruleitems discovery, rule generation and rule pruning. The results reveal that MRMCAR is superior to both current AC mining algorithms and rule based classification algorithms in improving the classification performance with respect to accuracy.

Ordered subtree mining via transactional mapping using a structure-preserving tree database schema

Frequent subtree mining is a major research topic in knowledge discovery from tree-structured data, whose importance is witnessed by the pervasiveness of such data in several domains. In this paper, we present a novel approach to discover all the frequent ordered subtrees in a tree-structured database. A key aspect is that the structural aspects of the input tree instances are extracted to generate a transactional format that enables the application of standard itemset mining techniques. In this way, the expensive process of subtree enumeration is avoided, while subtrees can be reconstructed in a post-processing stage. As a result, more structurally complex tree data can be handled and much lower support thresholds can be used. In addition to discovering traditional subtrees, this is the first approach to frequent subtree mining that can discover position-constrained subtrees. Each node in the position-constrained subtree is annotated with its exact occurrence and level of embedding in the original database tree. Also, disconnected subtree associations can be represented via virtual connecting nodes. Experiments conducted on synthetic and real-world datasets confirm the expected advantages of our approach over competing methods in terms of efficiency, mining capabilities, and informativeness of the extracted patterns.