Multiple Minimum Supports Research Articles

Towards Innovation of German Teaching via IoT Assistance and Virtual Classroom

The penetration of virtual classroom teaching into German teaching is the presentation of teaching innovation in the information age. In this work, we explore the necessity of applying virtual classroom teaching in German classrooms and the effective strategies of German teaching innovation in virtual classrooms, to provide some suggestions for the reform of German teaching. First, the GPS trajectories are transformed into a sequence of hotspot regions using the spatiotemporal properties of GPS points. Then, a sequential pattern mining algorithm of asynchronous cycles with multiple minimum supports based on pattern growth is adopted, and the sequential patterns of asynchronous cycles are deeply recursively mined according to the multiple minimum supports. Experiments show that the proposed IoT-assisted teaching scheme can effectively integrate equipment resources, mine spatiotemporal information, and help students and teachers establish a new educational method of integrating space and land. Compared with the baseline, it can fully exploit the characteristics of German.

Classification Algorithm for Library Electronic Documents Based on Continuous Attribute

The automatic classification of document data will occupy an increasingly important position in digital libraries. Generally, the kernel method based on support vector machine is used to classify literature data on the standard test set, which has some shortcomings. In order to solve these problems, vocabulary expansion is used to preprocess the document vector to obtain a small but precise, orthogonal, and unambiguous new document vector; the document vector is sorted according to semantics to improve the access and calculation speed; the document is mapped to Lz with the help of wavelet kernel space for document classification. This paper analyzes the existing continuous attribute discretization methods in detail, discusses how to reduce the loss of information in the discretization process, and proposes a low-frequency discretization (LFD) algorithm based on the attribute low-frequency region. This method effectively reduces data loss by setting the segmentation point in the attribute interval with lower frequency, and through the research and analysis of the existing association rule mining algorithm, this paper combines low-frequency discretization, weighted multiple minimum support, and full confidence, and a weighted multiple minimum support association rule mining algorithm based on low-frequency discretization (WM-SamplingHT) is proposed. The algorithm first uses the low-frequency discretization algorithm to discretize the continuous attributes, then sets the respective weights and minimum support for the data items when mining frequent itemsets, removes the false patterns through the full confidence, and then obtains cleaner frequent itemsets. Using the real classification data of China Academic Journals Network, it is verified from the perspectives of abstract information and full-text documents. The results show that this method is superior to the nuclear method and has certain theoretical research and practical applications.

Mining Frequent Patterns Partially Devoid of Dissociation with Automated MMS Specification Strategy

Mining frequent patterns with single minimum support is quite unpredictable as it may miss rare itemsets depending upon the high value of minimum threshold. The phenomenon is commonly known as “rare item problem” dilemma. In this connection, frequent pattern mining under multiple minimum supports (MMS) is considered as an adequate solution of the problem. Existing approaches in this domain require user-defined minimum item support (MIS), and consider only the support in recognizing the importance of the items and ignore other factors including the inter-item relationships. The generation of huge number of itemsets including the itemsets with low associability or high dissociation is another problem in the existing MMS-based approaches. Keeping view of the above issues, this paper introduces AutoMMS-FPM, an automated MMS specification approach for frequent pattern mining with partial devoid of dissociation. The proposed approach not only takes account of support but also some other factors such as influence (inf), dissociation (d) and length importance factor (ρ) to boost up the mining process. Influence refers to the inter-item relationship and it is formulated based on the concept of degree centrality of the corresponding item network. MIS for the items are calculated automatically using MBS and RIF factors where the first one relates to the item support and the second one relates to the item influence. Experimental results on both of the synthetic and real datasets show that the proposed algorithm outperforms than existing approaches in terms of number of generated itemsets, run time, memory usages, dissociation and scalability.

The Probability Apriori Multiple Minimum Support Algorithm on the Grid Environment

The Probability Apriori Multiple Minimum Support Algorithm on the Grid Environment

Spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth in Internet of Things

The temporal and spatial characteristics of users are involved in most Internet of Things (IoT) applications. The spatial and temporal movement patterns of users are the most direct manifestation of the temporal and spatial characteristics. The user’s interests, activities, experience and other characteristics are reflected by mobile mode. In view of the low clustering efficiency of moving objects in convergent pattern mining in the IoT, a spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth is proposed. Based on the temporal characteristics of user trajectories, frequent and asynchronous periodic spatiotemporal movement patterns are mined. Firstly, the location sequence is modeled, and the time information is added to the model. Then, a mining algorithm of asynchronous periodic sequential pattern is adopted. The algorithm is based on multiple minimum supports of pattern growth. According to multiple minimum supports, the sequential pattern of asynchronous period is mined deeply and recursively. Finally, the proposed method is validated and evaluated by Gowalla dataset, in which the user characteristics are truly reflected. It is shown by the experimental results that the average pointwise mutual information (PWI) of the proposed algorithm reaches 0.93. And the algorithm is proved to be effective and accurate.

Improving rule-based classification using Harmony Search.

Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, etc. Numerous previous studies have shown that this type of classifier achieves a higher classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, Harmony Search, and classification-based association rules (CBA) algorithm in order to build a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary Harmony Search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on a seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.

Discovering high average utility itemsets with multiple minimum supports

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Mining negative sequential patterns from infrequent positive sequences with 2-level multiple minimum supports

Negative sequential patterns (NSP) referring to both occurring items (positive items) and nonoccurring items (negative items) play a very important role in many real applications. Very few methods have been proposed to mine NSP and most of them only mine NSP from frequent positive sequences, not from infrequent positive sequences (IPS). In fact, many useful NSP can be mined from IPS, just like many useful negative association rules can be obtained from infrequent itemsets. e-NSPFI is a method to mine NSP from IPS, but its constraint is very strict to IPS and many useful NSP would be missed. In addition, e-NSPFI only uses a single minimum support, which implicitly assumes that all items in the database are of the similar frequencies. In order to solve the above problems and optimize NSP mining, a 2-level multiple minimum supports (2-LMMS) constraint to IPS is proposed in this paper. Firstly, we design two minimum supports constraints to mine frequent and infrequent positive sequences. Secondly, we use Select Actionable Pattern (SAP) method to select actionable NSP. Finally, we propose a corresponding algorithm msNSPFI to mine actionable NSP from IPS with 2-LMMS. Experiment results show that msNSPFI is very efficient for mining actionable NSP.

Mining negative sequential patterns from frequent and infrequent sequences based on multiple level minimum supports

Negative sequential patterns (NSP) are critical and sometimes much more informative than positive sequential patterns (PSP) in many intelligent systems and applications. However, the existing NSP algorithms do not allow negative items being contained in an element except the NegI-NSP algorithm, which can obtain many meaningful sequences with negative items in an element. NegI-NSP, however, hasn?t considered the following problems: (1) it uses a single minimum support to all size sequences, which is unfair to a long size sequence; (2) it only mines NSP from PSP, not from infrequent positive sequences (IPS), which also contain many useful NSP. So we propose an efficient algorithm, named MLMS-NSP, to mine NSP based on multiple level minimum supports (MLMS) from PSP and IPS. Firstly, MLMS scheme is proposed by assigning different minimum supports to sequences with different sizes. Secondly, IPS are constrained by combining MLMS, and then the NSP is obtained from these IPS. Finally, experimental results show that the MLMS-NSP algorithm can effectively mine NSP from IPS, and the time efficiency is higher than using single minimum support.

Mining High-Utility Itemsets Based on Multiple Minimum Support and Multiple Minimum Utility Thresholds

Mining High-Utility Itemsets Based on Multiple Minimum Support and Multiple Minimum Utility Thresholds

Mining of frequent patterns with multiple minimum supports

Frequent pattern mining (FPM) is an important topic in data mining for discovering the implicit but useful information. Many algorithms have been proposed for this task but most of them suffer from an important limitation, which relies on a single uniform minimum support threshold as the sole criterion to identify frequent patterns (FPs). Using a single threshold value to assess the usefulness of all items in a database is inadequate and unfair in real-life applications since each item is different and not all items should be treated as the same. Several algorithms have been developed for mining FPs with multiple minimum supports but most of them suffer from the time-consuming problem and require a large amount of memory. In this paper, we address this issue by introducing the novel approach named Frequent Pattern mining with Multiple minimum supports from the Enumeration-tree (FP-ME). In the developed Set-Enumeration-tree with Multiple minimum supports (ME-tree) structure, a new sorted downward closure (SDC) property of FPs and the least minimum support (LMS) concept with multiple minimum supports are used to effectively prune the search space. The proposed FP-ME algorithm can directly discover FPs from the ME-tree without candidate generation. Moreover, an improved algorithm, named FP-MEDiffSet, is also developed based on the DiffSet concept, to further increase mining performance. Substantial experiments on both real-life and synthetic datasets show that the proposed algorithms can not only avoid the “rare item problem”, but also efficiently and effectively discover the complete set of FPs in transactional databases while considering multiple minimum supports and outperform the state-of-the-art CFP-growth++ algorithm in terms of execution time, memory usage and scalability.

E-msNSP: Efficient Negative Sequential Patterns Mining Based on Multiple Minimum Supports

Mining negative sequential patterns (NSP) has been an important research area in data mining and knowledge discovery and it is much more challenging than mining positive sequential patterns (PSP) due to the computational complexity and search space. Only a few methods have been proposed to mine NSP and most of them only use single minimum support, which implicitly assumes that all items in the database are of the same nature or of similar frequencies in the database. This is often not the case in real-life applications. There are several methods to mine sequential patterns with multiple minimum supports (MMS), but these methods only consider PSP and do not handle NSP. So in this paper, we propose a new method, called e-msNSP, to mine NSP with multiple minimum supports. We also solve the problem of how to set up the minimum support to a sequence with negative item(s). E-msNSP consists of three major steps: (i) using the improved MS-GSP method to mine PSP with multiple minimum supports and storing all positive sequential candidates’ (PSC) related information simultaneously; (ii) using the same method in e-NSP to generate negative sequential candidates (NSC) based on above mined PSP; (iii) calculating the support of these NSC based only on the corresponding PSP and then getting NSP. To the best of our knowledge, e-msNSP is the first method to mine NSP with MMS and does not impose strict constraints. Experimental results show that the e-msNSP is highly effective and efficient.

Rule grouping and multiple minimum support thresholds for semantic multi-label associative classifier using feature reoccurrences

Multi-label classification is one of the important tasks in data mining. Researchers have addressed and extensively studied supervised classification which has vast applications in many domains. Associative classifiers are better performing classifiers, but they still have some issues which need to be addressed. This paper handles class imbalance problem, semantically organises vast number of generated rules, and applies relevant rules during classification. An algorithm called semantic multi-label associative classifier using feature reoccurrences (SeMACR) is proposed. Considering reoccurrence of features while generating rules proves to be beneficial, in particular for text documents. Class imbalance problem is handled with the help of balanced training and use of multiple minimum support thresholds based on the class distribution. A novel semantic-based approach is proposed for grouping of association rules using relatedness score between features rather than the traditional distance-based measure. Such organisation of rules makes them manageable and interpretable. During classification, only the relevant rules i.e., the rules present in the semantically most related group are applied. SeMACR algorithm has shown improved or comparable performance as compared to state-of-the-art techniques.

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Association rule mining, one of the most important and well-researched techniques of data mining. Mining frequent itemsets are one of the most fundamental problems and most time-consuming in association rule mining. Most of the algorithms in literature used to find frequent itemsets satisfying single minimum support threshold. In practice, frequentcy of each item reflects the nature and role of items in transactional databases. This paper proposes an efficient mining parallel algorithm for frequent itemsets with multiple minimum support thresholds (a different minimum item support for each item) on Multiple-core Processors. Proposed algorithm easily extends on distributed computing systems as Hadoop, Spark. Finally, result experiments presented on both synthetic and real-life datasets show the better proposed algorithm than the existing algorithms.

Rule grouping and multiple minimum support thresholds for semantic multi-label associative classifier using feature reoccurrences

Multi-label classification is one of the important tasks in data mining. Researchers have addressed and extensively studied supervised classification which has vast applications in many domains. Associative classifiers are better performing classifiers, but they still have some issues which need to be addressed. This paper handles class imbalance problem, semantically organises vast number of generated rules, and applies relevant rules during classification. An algorithm called semantic multi-label associative classifier using feature reoccurrences (SeMACR) is proposed. Considering reoccurrence of features while generating rules proves to be beneficial, in particular for text documents. Class imbalance problem is handled with the help of balanced training and use of multiple minimum support thresholds based on the class distribution. A novel semantic-based approach is proposed for grouping of association rules using relatedness score between features rather than the traditional distance-based measure. Such organisation of rules makes them manageable and interpretable. During classification, only the relevant rules i.e., the rules present in the semantically most related group are applied. SeMACR algorithm has shown improved or comparable performance as compared to state-of-the-art techniques.

Automatic Firewall Rule Generator for Network Intrusion Detection System based on Multiple Minimum Support

Background: Association rule mining plays a vital role in predicting the attacks and generating the firewall rules automatically. Data mining techniques discover the knowledge by counting the frequently occurring items, whereas most of the real-world datasets are non-uniform containing both frequently and relatively rarely occurring items. This paper discusses about how to generate the automatic firewall rules to detect anomalies using multiple minimum support. Methods: Mining association rules based on single minimum support approach suffers from the dilemma known as ‘rare item problem’ it requires multiple scans of database which increase the load and time consuming. To avoid this problem Multiple Minimum Support with Probability based approach (MMSP) is used to generate rules. Findings: To create a model of current user behavior from the dataset the probability will be compute with threshold value and the alarm will be raised accordingly. By using MMSP, the number of false alarm are reduced during intrusion detection and automatic firewall rules will be generated.

Building an associative classifier with multiple minimum supports.

Classification is one of the most important technologies used in data mining. Researchers have recently proposed several classification techniques based on the concept of association rules (also known as CBA-based methods). Experimental evaluations on these studies show that in average the CBA-based approaches can yield higher accuracy than some of conventional classification methods. However, conventional CBA-based methods adopt a single threshold of minimum support for all items, resulting in the rare item problem. In other words, the classification rules will only contain frequent items if minimum support (minsup) is set as high or any combinations of items are discovered as frequent if minsup is set as low. To solve this problem, this paper proposes a novel CBA-based method called MMSCBA, which considers the concept of multiple minimum supports (MMSs). Based on MMSs, different classification rules appear in the corresponding minsups. Several experiments were conducted with six real-world datasets selected from the UCI Machine Learning Repository. The results show that MMSCBA achieves higher accuracy than conventional CBA methods, especially when the dataset contains rare items.

Analysis and Implementation some of Data Mining Algorithms by Collecting Algorithm based on Simple Association Rules

analysis is utilized to detect the learning and set up tenets from a huge dataset. The minimum support value in the association investigation is a discriminating element to influence the execution of this detection. Association rule mining represent to a data mining method and its objective is to discover intriguing association or correlation relationships among a huge set of data elements. In this paper new algorithm has been proposed which to collecting the (Sample Association Rules) taken from (Basic Apriori Algorithm) with the (Multiple Minimum Support utilizing Maximum Constraints Algorithms). The algorithm is executed, and is compared with its other algorithms, using a new proposed comparison algorithm. Comparisons have been on various groups of data. Consequences of applying the proposed algorithm indicate speedier implementation than different algorithms. At the end, both of execution and results shows: Effortlessness, exactness, and velocity to new algorithm, as well as reliability of the another algorithms.

Efficient mining of high utility pattern with considering of rarity and length

Techniques for mining rare patterns have been researched in the association rule mining area because traditional frequent pattern mining methods have to generate a large amount of unnecessary patterns in order to find rare patterns from large databases. One such technique, the multiple minimum support threshold framework was devised to extract rare patterns by using a different minimum item support threshold for each item in a database. Nevertheless, this framework cannot sufficiently reflect environments of the real world. The reason is that it does not consider weights of items, such as market prices of products and fatality rates of diseases, in its mining process. Therefore, an algorithm has been proposed to mine rare patterns with utilities exceeding a user-specified minimum utility by considering rarity and utility information of items. However, since this algorithm employs the concept of traditional high utility pattern mining, patterns' lengths are not considered for determining utilities of the patterns. If the length of a pattern is sufficiently long, the pattern is more likely to have an enough utility to become a high utility pattern regardless of item utilities within the pattern. Therefore, the algorithm cannot guarantee that all items in a mined pattern have high utilities. In this paper, we propose a novel algorithm that effectively reduces such dependency of patterns on their lengths by considering their lengths in the mining process in order to mine more meaningful rare patterns compared to patterns mined by previous algorithms. Experimental results demonstrate that our algorithm extracts a lesser number of more meaningful patterns and consumes less computational resources compared to state-of-the-art algorithms.

Multiple Minimum Support-Based Rare Graph Pattern Mining Considering Symmetry Feature-Based Growth Technique and the Differing Importance of Graph Elements

Frequent graph pattern mining is one of the most interesting areas in data mining, and many researchers have developed a variety of approaches by suggesting efficient, useful mining techniques by integration of fundamental graph mining with other advanced mining works. However, previous graph mining approaches have faced fatal problems that cannot consider important characteristics in the real world because they cannot process both (1) different element importance and (2) multiple minimum support thresholds suitable for each graph element. In other words, graph elements in the real world have not only frequency factors but also their own importance; in addition, various elements composing graphs may require different thresholds according to their characteristics. However, traditional ones do not consider such features. To overcome these issues, we propose a new frequent graph pattern mining method, which can deal with both different element importance and multiple minimum support thresholds. Through the devised algorithm, we can obtain more meaningful graph pattern results with higher importance. We also demonstrate that the proposed algorithm has more outstanding performance compared to previous state-of-the-art approaches in terms of graph pattern generation, runtime, and memory usage.