Frequent Sets Research Articles

Application of Computer Data Mining Technology in the Analysis of Emotional Law

In recent years, with the in-depth development of the Internet, computer data mining technology has gradually matured, social networks have been integrated into people’s daily lives, and more and more Internet users have expressed their personal emotions and commented on social hotspots on social networks. In this regard, social network public opinion analysis plays a vital role, and has attracted widespread attention from academia and industry. In order to better excavate Weibo users’ opinions and ideas on social events, and track the development of the events, it is more conducive to the work of public opinion monitoring and rumor control. This paper expounds the research status of data mining and the current situation of psychological problems of college students. It introduces computer data mining technology into the analysis of college students’ emotional regularity, and introduces the concepts, functions, technologies, methods and processes of data mining. This paper proposes a clustering method based on the characteristics of the semantic recessive sentiment of frequent itemsets, which fully considers the implicit sentiment regularity of semantics in Weibo. Firstly, we define the frequent feature word set of Weibo based on the characteristics of dominant sentiment, and use the maximum frequent items. Clusters to obtain initial clusters of regular emotions; for text overlap between initial clusters, an inter-cluster overlap reduction algorithm based on short text extended semantic membership is proposed to obtain completely separated initial clusters; based on cluster semantic similarity matrix, agglomerated emotions are given Regular clustering method. Finally, the validity of the text method is verified by the training corpus data provided by the NLP & CC2013 evaluation. It can be known from the test results that when the minimum support degree θ of the cluster is 0.5-0.6, the clustering effect of the analysis of sentiment regularity is better.

MOOC opinion mining based on attention alignment

PurposeThe purpose of this paper is to propose an attention alignment method for opinion mining of massive open online course (MOOC) comments. Opinion mining is essential for MOOC applications. In this study, the authors analyze some of bidirectional encoder representations from transformers (BERT’s) attention heads and explore how to use these attention heads to extract opinions from MOOC comments.Design/methodology/approachThe approach proposed is based on an attention alignment mechanism with the following three stages: first, extracting original opinions from MOOC comments with dependency parsing. Second, constructing frequent sets and using the frequent sets to prune the opinions. Third, pruning the opinions and discovering new opinions with the attention alignment mechanism.FindingsThe experiments on the MOOC comments data sets suggest that the opinion mining approach based on an attention alignment mechanism can obtain a better F1 score. Moreover, the attention alignment mechanism can discover some of the opinions filtered incorrectly by the frequent sets, which means the attention alignment mechanism can overcome the shortcomings of dependency analysis and frequent sets.Originality/valueTo take full advantage of pretrained language models, the authors propose an attention alignment method for opinion mining and combine this method with dependency analysis and frequent sets to improve the effectiveness. Furthermore, the authors conduct extensive experiments on different combinations of methods. The results show that the attention alignment method can effectively overcome the shortcomings of dependency analysis and frequent sets.

On the design of hardware architectures for parallel frequent itemsets mining

Algorithms for Frequent Itemsets Mining have proved their effectiveness for extracting frequent sets of patterns in datasets. However, in some specific cases, they do not obtain the expected results in an acceptable time. For this reason, Field Programmable Gates Array-based architectures for Frequent Itemsets Mining have been proposed to accelerate this task. The current paper proposes a search strategy for Frequent Itemsets Mining based on equivalence classes partitioning. The partitioning on equivalence classes allows dividing the search space into disjoint sets that can be processed in parallel. Consequently, this paper presents the design and implementation of two hardware architectures that exploit the nested parallelism in the proposed search strategy. These hardware architectures are capable of obtaining frequent itemsets regardless of the number of distinct items and the number of transactions in the dataset, which are the main issues reported in the reviewed literature. Furthermore, the proposed architectures explore the trade-off between acceleration and hardware resource utilization. The experimental results obtained demonstrate that the proposed search strategy can be scaled to achieve a speedup in the processing time of 40 times faster than software-based implementations.

Traffic Signal Data for Emergency Vehicles using C-Means and SVM Classification

Traffic related troubles include not just traffic jamming due to increase in vehicular density, but also complexity for passage of emergency vehicles, violation of rules such as red signal jumps, vehicle breakdowns and accidents causing blockage of roads and loss of lives. Nowadays lot of people losing their lives due to delay of emergency vehicle service. By providing ambulance service timely and accurate can reduce the deaths. By avoiding the unnecessary time delay near traffic jams during an emergency situation. Clustering is a machine learning procedure that includes the gathering of targeted information which is a strategy for unsubstantiated learning and is a typical procedure for factual information investigation utilized in numerous fields. Fuzzy C-means logic is a technique for clustering which enables one bit of information to have a place with two or more clustering. The proposed Fuzzy C-Means (FCM) algorithm technique is often utilized calculation, to inspect the different types of information with the frequent data sets. The Support Vector Machine (SVM) classification method is obviously used classification model which classifies the data entirely however the size is in a common manner. In this paper, a set of datasets is implanted and the experimental clustering report is verified with the frequent parameters such as overlapping, data partitioning, high dimensional data and irrelevant data clustering. On comparing with existing clustering processes, this proposed approach shows the high efficiency than other clustering models with approximate effective results on the association rules.

The Frequent Network Neighborhood Mapping of the human hippocampus shows much more frequent neighbor sets in males than in females.

In the study of the human connectome, the vertices and the edges of the network of the human brain are analyzed: the vertices of the graphs are the anatomically identified gray matter areas of the subjects; this set is exactly the same for all the subjects. The edges of the graphs correspond to the axonal fibers, connecting these areas. In the biological applications of graph theory, it happens very rarely that scientists examine numerous large graphs on the very same, labeled vertex set. Exactly this is the case in the study of the connectomes. Because of the particularity of these sets of graphs, novel, robust methods need to be developed for their analysis. Here we introduce the new method of the Frequent Network Neighborhood Mapping for the connectome, which serves as a robust identification of the neighborhoods of given vertices of special interest in the graph. We apply the novel method for mapping the neighborhoods of the human hippocampus and discover strong statistical asymmetries between the connectomes of the sexes, computed from the Human Connectome Project. We analyze 413 braingraphs, each with 463 nodes. We show that the hippocampi of men have much more significantly frequent neighbor sets than women; therefore, in a sense, the connections of the hippocampi are more regularly distributed in men and more varied in women. Our results are in contrast to the volumetric studies of the human hippocampus, where it was shown that the relative volume of the hippocampus is the same in men and women.

A Novel Approach for Associative Classification Based on Information Entropy of Frequent Attribute Set

Associative Classification (AC) is envisioned as one of the most attractive classification approaches to facilitate managers for prediction and decision making in a highly accurate and easily interpretable way. The most of existing AC algorithms mainly focus on the two static metrics of association rules: support and confidence. However, in this paper, we identify a potential limitation of the confidence and further point out these AC algorithms merely consider the explicit expression of classification information and knowledge of Class Association Rules (CARs) and neglect the implicit expression of classification information and knowledge of CARs. And even the explicit expression of classification information and knowledge of CARs is equivalent, the implicit expression of classification information and knowledge of them may be different. Thus, these CARs will cause different influence on prediction or decision-making in terms of the interpretability and rationality of classification, and will result in predictive bias or decision-making bias in practice. In response to this drawback, we introduce the notion of information entropy and propose an innovative approach for associative classification based on information entropy of frequent attribute set, named EAC. Different from the existing schemes, the proposed EAC algorithm enjoys the following promising merits: (1) lower predictive bias or decision-making bias by levering the information entropy of frequent attribute set; (2) better interpretability and rationality by setting higher support threshold; (3) setting global optimum parameter dynamically through repeated trials with reducing the dimension of data sets. Experiments on 20 well-known benchmark data sets demonstrate that our EAC approach is highly competitive to other state-of-the-art AC algorithms in terms of predictive bias or decision-making bias, interpretability, and efficiency, which can be used to construct a new classifier efficiently and effectively in many realistic scenarios.

Corpus Investigations of Medieval Slavonic Manuscripts: Statistically Important N-Grams (Collocations) of Old Russian Chronicles

The paper deals with the current status of preparation of Slavonic historical textual corpora and requirements for them from the point of view of processing, search and demonstration of linguistic data. It is stressed that the main causes of the slow development of this line are high labor expenditures of manual creation of machine-readable transcriptions and their tagging and the necessity of training of special corpus managers providing access to data and its visualization. It is emphasized that one of the lines of use of corpus data of current importance is its analysis with the help of quantitative and statistic methods. There is a description of the functional possibilities of the historical corpus “Manuscript” comprising medieval Slavonic manuscripts of the 10th — 15th centuries (manuscripts.ru). The possibilities of the module of n-grams for revelation of grammatically and semantically set expressions characterizing the text subjects are demonstrated on the example of subcorpus of three Old Russian chronicles (Laurentian, Hypatian, Radzivilovsky). Statistic measures Mutual Information and T-score help to reveal the lists of relatively rare and more frequent set expressions. MI-lists include proper names, pair names, set biblical and Slavonic-bookish subordinating constructions. T-score lists give information on the events, goals, persons, outputs and their characteristics. A conclusion on the efficiency of application of statistic measures to automatic finding of the semantically and thematically important expressions in the historical sources is made.

Penerapan Metode Association Rule Untuk Menganalisa Pola Pemakaian Bahan Kimia Di Laboratorium Menggunakan Algoritma FP-Growth (Studi Kasus di Laboratorium Kimia PT. PLN (Persero) Sektor Pembangkitan Belawan Medan)

<p class="Default"><em>The pattern of using chemicals in the laboratory of PT. PLN (Persero) </em><em>Sektor Pembangkitan </em><em>Belawan Medan is not only to find out what chemicals are used but also to find out the amount of chemicals left so that laboratory officials can properly manage the use of these chemicals. One appropriate way to determine the pattern of use of these chemicals is to use data mining techniques. The Data Mining technique used in this case is the FP-Growth Algorithm. FP-Growth is an alternative algorithm that can be used to determine the most frequent set of data in a data set. The study was conducted using several variables, namely the date and chemicals used. The results of this study are in the form of a chemical usage pattern which is processed using software, namely implementing the FP-Growth algorithm using the concept of FP-Tree development in searching for Frequent Itemset.</em></p><p class="Default"><em> </em></p><pre><em>Keywords: Data Mining, Association Rules, Frequent Itemset, FP-Growth</em></pre>

Maximum Frequent Item Set based Clustering Algorithm for Big Text Data

Due to fast growth of internet and continuous expansion of World Wide Web like digital libraries, online news contributes to massive amount of electronic unstructured text documents on the web. Although lot traditional techniques are available to extract the knowledge from large collection of text documents, still to improve precision of the web search retrieval and to find most appropriate documents from huge text collections proficiently is a big challenge. Clustering techniques helps the search engine to retrieve the documents. The proposed system overcomes existing problems using bivariate n-gram frequent item clustering algorithm by concept of maximum frequent set which maintain the sequence and meaning of sentence in order to reduce huge dimension and and frequent item sets finds similarity. Then based on maximum document occurrence we cluster the documents. Thus our method obtains quality of clusters when compared with existing methodologies and improves the efficiency. The experiment is shown for sample Newsgroup dataset for existing K-Mean and FICMDO (Frequent item clustering method based on maximum document occurrence) and proved the f-measure is higher for our algorithm. Since the f-measure increases, obtains efficient clusters. Hence it is faster and efficient big data method which improves the performance when compared with vector space model like K-Means algorithm.

WITHDRAWN: Video structure syntax mining based on Hidden Markov Model in sports video

Abstract On the basis of shot segmentation, an improved Rate Sensitive Competitive Learning algorithm (RSCLA) is proposed for unsupervised shot clustering based on NBA basketball video, which converts video stream data into symbol sequence. Aiming at the characteristics of sequence correlation, time correlation and no clear transaction concept in Video Association rules, this paper improved the traditional Apriori algorithm and proposed a video Association Rules Mining Algorithm based on time-based window computing support. The periodic or semi-periodic structural grammar patterns in video are explored with frequent sets of association rules to guide the establishment of video structure semantic mining model. Aiming at the limitation of string matching, this paper proposes a HMM-based pattern mining method to analyze high-level video events, identify and locate free throw events in basketball videos. The experimental results show that the four-state HMM is the inner free throw mode of basketball video and achieves good performance.

Smartphone customer segmentation based on the usage pattern

The dimension used to measure user heterogeneity plays a key role in the customer segmentation. For most traditional products, customer requirements (CRs) for products are often related to their basic characteristics and psychological characteristics. Therefore, in the traditional market segmentation theory, the dimensions used to distinguish customer differences include the demographic attributes such as gender, age, income etc., or the customer psychology. Customer behaviour, especially the purchasing behaviour, is also used as an important dimension for market segmentation. However, for the smart product like smartphone with rich functionality and multi-interactions, the customer’s interaction preference can be fully released. That makes the heterogeneity of customer stems from the usage characteristics rather than the traditional demographic attributes. Hence, the usage pattern is defined and proposed as the description of the usage characteristics and be used to measure the heterogeneity of customers in this research. The Equivalence CLAss Transformation (ECLAT) algorithm is employed to identify the customer’s APP frequent sets from the operating data and to construct the usage pattern. Thereafter, the customer can be segmented based on the distance among customers’ usage pattern. Compared with the demographic attributes, the usage pattern can provide more reliable and truthful measures for the smartphone customer segmentation.

Recommendation-Based Component Selection for Component-Based Systems

Selection of reusable software components in software repositories to facilitate quality software development has always been a focal point and a big concern for software researchers. One of the most time-consuming tasks in software reusability is tracing and retrieving software components from a large repository. The selection of inapt software package can result in high cost and ultimately becomes a prime source of adverse outcome in business processes and performance of the organization. Creation of quality software depends upon the selection of the best set of components among all the alternatives present in the reusable component repository. Frequently used sets of components can be obtained by using mining algorithms. This paper proposes a component selection methodology and a method for retrieval of the optimal set of reusable components from the repository. Case-based retrieval is applied for initial filtering of components to narrow down the search space. A data mining algorithm is applied to extract the candidate set of components for a given case. Most frequent set of components aid the decision-maker to select the finest component set and also assist in suggesting the supplementary components for the case to match with the latest updates.

Aplikasi Web Usage Mining Menggunakan Metode Association Rule Dengan Algoritma Fp-Growth Untuk Mengetahui Pola Browsing Pengunjung Website

As a way to improve the promotion of institutions via the web, there is a need for a method to view browsing patterns of visitors on the site unilak.ac.id, thereby showing the user's interest in the links he visits. Data mining or knowledge discovery is a process of extracting valuable information by analyzing the existence of certain patterns or relationships. To find visitor patterns in the form of association rules is to use the association rule method. FP-Growth is an alternative algorithm that can be used to determine the most frequent set of data in a set of data. FP-Growth is applied to get a pattern of visitors, about what links are frequently visited and seen by visitors on the site unilak.ac.id. This pattern is used to help web administrators in developing the site unilak.ac.id by utilizing knowledge from the association pattern to regulate the layout / layout design of the categories available on the site unilak.ac.id. From the results of processing the dataset with FP-Growth algorithm and processing data processed using data mining software, namely Rapidminer 6.5. It was found that the minimum value of support was 1% and the minimum confidence value of 50% resulted in 124 rules of association.

Credit Card User Frequent Buying Predicton Analysis using Cluster Methods

Today the world becomes more digital. The cashless transactions are increased in all sectors. The large amounts of data in digital form are generated every day. The companies need to analyze the existing transactions, to predict the user requirements in the future. The payment during the purchase can be done in different modes by the user. In this work, the credit card transactions are analyzed. There are many data mining techniques are used to predict the frequent sets of items during purchase. Data clustering in one of the familiar and widely used technique to identify a similar set of items in a group or dataset. In this work, the two familiar existing techniques k-means and k-mediods are compared with the same datasets. The results show the best clustering algorithm.

Exploiting patterns to explain individual predictions

Users need to understand the predictions of a classifier, especially when decisions based on the predictions can have severe consequences. The explanation of a prediction reveals the reason why a classifier makes a certain prediction, and it helps users to accept or reject the prediction with greater confidence. This paper proposes an explanation method called Pattern Aided Local Explanation (PALEX) to provide instance-level explanations for any classifier. PALEX takes a classifier, a test instance and a frequent pattern set summarizing the training data of the classifier as inputs, and then outputs the supporting evidence that the classifier considers important for the prediction of the instance. To study the local behavior of a classifier in the vicinity of the test instance, PALEX uses the frequent pattern set from the training data as an extra input to guide generation of new synthetic samples in the vicinity of the test instance. Contrast patterns are also used in PALEX to identify locally discriminative features in the vicinity of a test instance. PALEX is particularly effective for scenarios where there exist multiple explanations. In our experiments, we compare PALEX to several state-of-the-art explanation methods over a range of benchmark datasets and find that it can identify explanations with both high precision and high recall.

WordCode using WordTrie

Computers work with text data by assigning a code for each character, called encoding. Character-encoding techniques emerged in the late 1960s, and a similar type of technique is still used to encode text data. Computers can only understand alphabets, not words. In this article, we develop an approach that enables computers to understand words. We introduce a word-based encoding of text data named WordCode. WordCode encodes the most frequent set of characters (i.e., words) found in Internet directories with a dynamic code combination. Although some dictionary-encoding techniques have been proposed, we still tend to use character encoding, such as Unicode, to encode text data. Dictionary-encoding techniques have not been adopted due to the massive size of the code page and the complexity in accessing the code page. In this article, we introduce a customised trie named WordTrie to store words for faster encoding and decoding. We generate the code combination in such a way that the size of the WordCode for a word is always smaller than the total size of the character coding. Our experimental results from encoding text files from the Gutenberg corpus, Canterbury corpus, large corpus, Calgary corpus and Silesia corpus using WordCode show an up to 19.9% reduction in file size with respect to character-based encoding. This smaller file size means that less storage space is needed and results in faster processing and communication of text data.

Trajectory big data processing based on frequent activity

With the rapid development and wide use of Global Positioning System in technology tools, such as smart phones and touch pads, many people share their personal experience through their trajectories while visiting places of interest. Therefore, trajectory query processing has emerged in recent years to help users find their best trajectories. However, with the huge amount of trajectory points and text descriptions, such as the activities practiced by users at these points, organizing these data in the index becomes tedious. Therefore, the parallel method becomes indispensable. In this paper, we have investigated the problem of distributed trajectory query processing based on the distance and frequent activities. The query is specified by start and final points in the trajectory, the distance threshold, and a set of frequent activities involved in the point of interest of the trajectory. As a result, the query returns the shortest trajectory including the most frequent activities with high support and high confidence. To simplify the query processing, we have implemented the Distributed Mining Trajectory R-Tree index (DMTR-Tree). For this method, we initially managed the large trajectory dataset in distributed R-Tree indexes. Then, for each index, we applied the frequent itemset Apriori algorithm for each point to select the frequent activity set. For the faster computation of the above algorithms, we utilized the cluster computing framework of Apache Spark with MapReduce as the programing model. The experimental results show that the DMTR-Tree index and the query-processing algorithm are efficient and can achieve the scalability.

Application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint

The basic idea of Apriori algorithm is first introduced in this paper, which is to find all frequent sets in a transaction. The frequent requirements of these frequent sets are greater than or equal to the minimum support of the set. On this basis, the working principle of the traditional Apriori algorithm is analyzed, and the existing problems are pointed out. To solve these problems, an improved Apriori algorithm is proposed for time series of frequent itemsets. Finally, on the basis of analyzing the methods and processes of mining association rules for time series, this improved time series Apriori algorithm for frequent itemsets is applied to mining association rules based on time constraints. The experimental results show that the improved Apriori algorithm is better than the traditional one in storage space.

Mining Frequent Link Sets from Web Log Using Apriori Algorithm

Mining Frequent Link Sets from Web Log Using Apriori Algorithm

Shorter But More Frequent Rest Periods: No Effect on Velocity and Power Compared to Traditional Sets Not Performed to Failure.

Performing traditional sets to failure is fatiguing, but redistributing total rest time to create short frequent sets lessens the fatigue. Since performing traditional sets to failure is not always warranted, we compared the effects of not-to-failure traditional sets and rest redistribution during free-weight back squats in twenty-six strength-trained men (28 ± 5.44 y; 84.6 ± 10.5 kg, 1RM-to-body-mass ratio of 1.82 ± 0.33). They performed three sets of ten repetitions with 4 min inter-set rest (TS) and five sets of six repetitions with 2 min inter-set rest (RR6) at 70% of one repetition maximum. Mean velocity (p > 0.05; d = 0.10 (-0.35, 0.56)) and mean power (p > 0.05; d = 0.19 (-0.27, 0.64)) were not different between protocols, but the rating of perceived exertion (RPE) was less during RR6 (p < 0.05; d = 0.93 (0.44, 1.40)). Also, mean velocity and power output decreased (RR6: 14.10% and 10.95%; TS: 17.10% and 15.85%, respectively) from the first repetition to the last, but the percentage decrease was similar (velocity: p > 0.05; d = 0.16 (0.30, 0.62); power: p > 0.05; d = 0.22 (-0.24, 0.68)). These data suggest that traditional sets and rest redistribution maintain velocity and power output to a similar degree when traditional sets are not performed to failure. However, rest redistribution might be advantageous as RR6 displayed a lower RPE.