Accurate Mining Research Articles

A VFDT algorithm optimization and application thereof in data stream classification

Data stream mining is a new technology for dynamically extracting feature patterns from data streams. Its core technology is data stream mining algorithms. Data stream mining algorithms are divided into clustering and classification algorithms. In data stream classification algorithms, VFDT (The Concept-adapting Very Fast Decision Tree algorithm is an effective classification decision tree algorithm. The algorithm dynamically constructs a decision tree based on the improvement of the Hoefdding tree. With the inflow of data, new branches are continuously added and outdated Branch. However, the algorithm has the problem of concept drift, and some nodes may no longer meet the Hoefdding bound, which affects the classification mining effect. This paper proposes an optimization algorithm (VFDT), which reduces the impact of concept drift and network noise by adding sliding window technology and fuzzy technology to the VFDT classification mining algorithm. Experimental results show that the algorithm can effectively improve the accuracy of stream data classification mining. Reduce classification errors.

Differentially Private Tensor Deep Computation for Cyber–Physical–Social Systems

In the recent past, deep learning has received remarkable acceptance in real-world applications. Social computing expands the existing notion of cyber space and physical space to a more advance cyber-physical-social system (CPSS). Therefore, deep learning provides a propitious technique for accurate mining of information from CPSS, thus facilitates CPSS to offer services of exceptional quality efficiently. However, most of the current deep learning methods are struggling to keep up with the ever-increasing heterogeneous and highly nonlinear dissemination of data. Furthermore, the advancement of deep learning presents privacy concerns. This article proposes a deep private tensor autoencoder (dPTAE), where tensors are used for data representation, and differential privacy guarantees strong privacy. The core idea of our work is to enforce differential privacy through noise injection into the objective functions instead of the results they produce. In addition, the proposed method preserves the privacy of information shared amongst CPSS in smart environments. We applied dPTAE on three representative data sets. Rigorous experimental evaluations and theoretical analysis demonstrate that dPTAE is significantly effective and efficient.

Intelligent mining algorithm for complex medical data based on deep learning

In order to address the problems of low precision, long time-consuming and low recall rate in mining complex attribute medical data in medical information, an intelligent mining algorithm for complex attribute medical data based on deep learning is proposed. Discretized medical data with complex attributes and converted it into a data type suitable for deep learning research, the convolutional neural network is used to analyze the association mapping relationship between complex attribute medical data sets and extract association rules of data. According to the degree of association between complex attribute medical data sets in multi-dimensional subspace to realize the effective mining of complex attribute medical data. The results show that the proposed algorithm takes less time and can extract association rules accurately, the data priority control efficiency is higher, the data mining accuracy is better, and the data mining recall rate is much higher than other methods, which verifies the feasibility of the proposed algorithm.

A cluster-classification method for accurate mining of seasonal honey bee patterns

Bees are the main pollinators of most wild plant species and insect-pollinated crops and are essential for both plant ecosystems maintenance and humans food production. Among the crops used for human feeding, 75% depend on pollination. In addition to the fact that uncertainty around the beekeeping activity could jeopardize the economic value of pollination, data on honey bee colony losses exist but have not been thoroughly and systematically analyzed to identify potential causal factors. Recognition of seasonal honey bee data patterns can be useful for a number of purposes such as swarming observations, and for forecasting colonies absconding - especially for those hives where the resources are scarce. Here we propose a method to identify honey bee seasonal patterns. The main aim of this research in identifying these patterns is to assist the beekeeper in the management and maintenance of their hives, and, additionally, to prove that with machine learning and, in particular, unsupervised learning is possible to detect seasonal honey bee patterns. We applied a clustering technique in two real datasets from HiveTool.net pursuing brood temperature, relative humidity, and beehives weight. From a clustering validation index and the k-means algorithm, we have found 6 coherent patterns related to seasons. From the found patterns, we compared three well-known classification algorithms (Naive Bayes, k-NN, and Random Forest) to propose a high accuracy classification model (hit rates up to 99.67%) that suggests seasonal honey bee patterns for remote monitoring computing systems.

Data association rules mining method based on genetic optimization algorithm

Traditional data mining methods usually need to visit the database repeatedly to determine the frequent item set, which makes the data server burden heavier and reduces the efficiency of data mining. To solve this problem, this paper combines the immune mechanism and genetic algorithm dynamically to improve the traditional genetic algorithm (GA), and proposes a data association rule mining method based on improved immune genetic algorithm (IIGA), which realize the effective analysis of big data. The experimental results show that the algorithm we proposed is better than immune genetic algorithm and Apriori algorithm in data mining time and association rules mining accuracy, which can be better applied to data analysis. The research results have positive reference significance for the field of data mining.

Mining Negative Comment Data of Microblog Based on Merge-AP

A new depiction method based on the merge-AP algorithm is proposed to effectively improve the mining accuracy of negative comment data on microblog. In this method, we first employ the AP algorithm to analyze negative comment data on microblog and calculate the similarity value and the similarity matrix of data points by Euclidean distance. Then, we introduce the distance-based merge process to solve the problem of poor clustering effect of the AP algorithm for datasets with the complex clustering structure. Finally, we compare and analyze the performance of K-means, AP, and merge-AP algorithms by collecting the actual microblog data for algorithm evaluation. The results show that the merge-AP algorithm has good adaptability.

Evolving network representation learning based on random walks

Large-scale network mining and analysis is key to revealing the underlying dynamics of networks, not easily observable before. Lately, there is a fast-growing interest in learning low-dimensional continuous representations of networks that can be utilized to perform highly accurate and scalable graph mining tasks. A family of these methods is based on performing random walks on a network to learn its structural features and providing the sequence of random walks as input to a deep learning architecture to learn a network embedding. While these methods perform well, they can only operate on static networks. However, in real-world, networks are evolving, as nodes and edges are continuously added or deleted. As a result, any previously obtained network representation will now be outdated having an adverse effect on the accuracy of the network mining task at stake. The naive approach to address this problem is to re-apply the embedding method of choice every time there is an update to the network. But this approach has serious drawbacks. First, it is inefficient, because the embedding method itself is computationally expensive. Then, the network mining task outcome obtained by the subsequent network representations are not directly comparable to each other, due to the randomness involved in the new set of random walks involved each time. In this paper, we propose EvoNRL, a random-walk based method for learning representations of evolving networks. The key idea of our approach is to first obtain a set of random walks on the current state of network. Then, while changes occur in the evolving network’s topology, to dynamically update the random walks in reserve, so they do not introduce any bias. That way we are in position of utilizing the updated set of random walks to continuously learn accurate mappings from the evolving network to a low-dimension network representation. Moreover, we present an analytical method for determining the right time to obtain a new representation of the evolving network that balances accuracy and time performance. A thorough experimental evaluation is performed that demonstrates the effectiveness of our method against sensible baselines and varying conditions.

An iterative end point fitting based trend segmentation representation of time series and its distance measure

Symbolic approximation representation is a key problem in time series which can significantly affect the accuracy and efficiency of data mining. However, since currently used methods divide the original sequence into segments with equal size, they ignore one of the most important features of time series: the trend. To overcome the defect of equal-sized segmenting, we present a trend segmentation representation based on Iterative End Point Fitting algorithm (IEPF-TSR). Particularly, we use iterative end point fitting (IEPF) algorithm to search the break point of each segment and get the trend segmentation. Then a triplet based symbolic representation is proposed for each segment which includes the start point, mean and trend. Moreover, we define a new distance measure method based on trend segmentation representation (TSR-DIST) which can suit for two representations with different lengths, and prove it to be the lower bound of Euclidean distance. The experimental results on UCR datasets show that the proposed representation and distance measure achieve better performance than the state-of-the-art methods in the classification accuracy and the dimensionality reduction ratio.

An Anomaly Data Mining Method for Mass Sensor Networks Using Improved PSO Algorithm Based on Spark Parallel Framework

Accurate detection and capture of anomaly data in complex network data stream is an important part of ensuring network security. Traditional methods cannot adapt to the high dynamic changes of abnormal data characteristics in complex network. Thus, the detection accuracy is reduced. In this paper, a k-means parallel clustering algorithm is proposed. It is optimized by particle swarm optimization with dynamic adaptive inertia weight (dsPSOK-means). And it is used to mine the anomaly data for mass sensor networks. The inertia weight is dynamically adjusted through the fitness function, so that the dsPSO algorithm has the adaptive characteristics. Then, the output of the dsPSO algorithm is taken as the input of the k-means algorithm. Thus, the intelligence and self-adaptability of the k-means algorithm in selecting the initial center point is improved. Finally, with the help of Spark platform, the parallelization of dsPSOK-means clustering algorithm in the clustering environment is designed and implemented. It is shown by the experimental results that the traffic among nodes in the execution process can be effectively reduced by the dsPSOK-means algorithm. And the accuracy of abnormal data mining in complex network data flow is 5% higher than that of the comparison algorithm on average.

Study on microblog public opinion data mining algorithm based on multi-visual clustering model

Because of the random distribution of microblog public opinion data, it is difficult to mine, this paper proposes a microblog public opinion data mining algorithm based on multi vision clustering model. It constructs the phase space distribution structure model of microblog public opinion data, the fuzzy association rule distribution set of microblog public opinion data, analyses the high-order statistical characteristics of microblog public opinion data, and advances the data of fuzzy clustering center according to the different statistical characteristics Row partition block scheduling. The binary structure of microblog public opinion data is reconstructed in the virtual database, and multi angle fuzzy clustering is carried out according to the reconstruction results to realise the optimised mining of microblog public opinion data. The simulation results show that the mining time of this method is up to 4.13 MS and the mining accuracy is up to 100%.

Study on microblog public opinion data mining algorithm based on multi-visual clustering model

Because of the random distribution of microblog public opinion data, it is difficult to mine, this paper proposes a microblog public opinion data mining algorithm based on multi vision clustering model. It constructs the phase space distribution structure model of microblog public opinion data, the fuzzy association rule distribution set of microblog public opinion data, analyses the high-order statistical characteristics of microblog public opinion data, and advances the data of fuzzy clustering center according to the different statistical characteristics Row partition block scheduling. The binary structure of microblog public opinion data is reconstructed in the virtual database, and multi angle fuzzy clustering is carried out according to the reconstruction results to realise the optimised mining of microblog public opinion data. The simulation results show that the mining time of this method is up to 4.13 MS and the mining accuracy is up to 100%.

DPARM: Differentially Private Association Rules Mining

Association analysis is critical in data analysis performed to find all co-occurrence relationships ($i.e$ ., frequent itemsets or confident association rules) from the transactional dataset. An association rule can improve the ability of users to discover patterns and develop corresponding strategies. The data analysis process can be summarized as a set of queries, where each query is a real-valued function of the dataset. However, unless restrictions and protections are implemented, accessing the dataset to answer the queries may lead to the disclosure of the private information of individuals. In this paper, we propose an original differentially private association rules mining (DPARM) algorithm, which uses multiple support thresholds to reduce the number of candidate itemsets while reflecting the real nature of the items and uses random truncation and uniform partition to reduce the dimensionality of the dataset. Both of these elaborated approaches can aid in reducing the sensitivity of the queries, and this dramatically reduces the scale of the required noise and improves the utility of the mining results. We significantly stabilize the noise scale by adaptively allocating the privacy levels and bound the overall privacy loss. Through a series of experiments, we prove that our DPARM algorithm outperforms the literature in the accuracy of data mining while satisfying differential privacy. To the best of our knowledge, our work is the first DPARM algorithm to adopt multiple support thresholds while using a set of elaborated approaches to bound the overall privacy loss of the mining process.

Multi-Source Data Stream Online Frequent Episode Mining

Online frequent episode mining is more complicated than the traditional static frequent episode mining due to the continuous, unbounded and time-varying data stream. Especially in the multiple data streams, online frequent episode mining is more difficult than the single-source stream, due to the concurrency, global clock loss, and uncertainty of delay caused by the distributed environment. To cope with these problems, we propose a new algorithm. Firstly, the data stream with “happen-before” relationship among multiple sources is combined on the global data lattice. Next, the traversal on global data lattice generates effective parallel and serial candidate data streams, which guarantee the accuracy of subsequent mining and reduce the number of global sequences during searching process. Then, we use the frequent episode tree to detect the expanding online serial episodes and parallel episodes. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments.

Using text mining to extract depressive symptoms and to validate the diagnosis of major depressive disorder from electronic health records

BackgroundMany studies have used Taiwan's National Health Insurance Research database (NHIRD) to conduct psychiatric research. However, the accuracy of the diagnostic codes for psychiatric disorders in NHIRD is not validated, and the symptom profiles are not available either. This study aimed to evaluate the accuracy of diagnostic codes and use text mining to extract symptom profile and functional impairment from electronic health records (EHRs) to overcome the above research limitations. MethodsA total of 500 discharge notes were randomly selected from a medical center's database. Three annotators reviewed the notes to establish gold standards. The accuracy of diagnostic codes for major psychiatric illness was evaluated. Text mining approaches were applied to extract depressive symptoms and function profiles and to identify patients with major depressive disorder. ResultsThe accuracy of the diagnostic code for major depressive disorder, schizophrenia, and dementia was acceptable but that of bipolar disorder and minor depression was less satisfactory. The performance of text mining approach to recognize depressive symptoms is satisfactory; however, the recall for functional impairment is lower resulting in lower F-scores of 0.774–0.753. Using the text mining approach to identify major depressive disorder, the recall was 0.85 but precision was only 0.69. ConclusionsThe accuracy of the diagnostic code for major depressive disorder in discharge notes was generally acceptable. This finding supports the utilization of psychiatric diagnoses in claims databases. The application of text mining to EHRs might help in overcoming current limitations in research using claims databases.

Federated Tensor Mining for Secure Industrial Internet of Things

In a vertical industry alliance, Internet of Things (IoT) deployed in different smart factories are similar. For example, most automobile manufacturers have the similar assembly lines and IoT surveillance systems. It is common to observe the industrial knowledge using deep learning and data mining methods based on the IoT data. However, some knowledge is not easy to be mined from only one factory's data because the samples are still few. If multiple factories within an alliance can gather their data together, more knowledge could be mined. However, the key concern of these factories is the data security. Existing matrix-based methods can guarantee the data security inside a factory but do not allow the data sharing among factories, and thus their mining performance is poor due to lack of correlation. To address this concern, in this article we propose the novel federated tensor mining (FTM) framework to federate multisource data together for tensor-based mining while guaranteeing the security. The key contribution of FTM is that every factory only needs to share its ciphertext data for security issue, and these ciphertexts are adequate for tensor-based knowledge mining due to its homomorphic attribution. Real-data-driven simulations demonstrate that FTM not only mines the same knowledge compared with the plaintext mining, but also is enabled to defend the attacks from distributed eavesdroppers and centralized hackers. In our typical experiment, compared with the matrix-based privacy-preserving compressive sensing (PPCS), FTM increases up to 24% on mining accuracy.

SENTIMEN ANALISIS KEBIJAKAN GANJIL GENAP DI TOL BEKASI MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN OPTIMALISASI INFORMATION GAIN

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Naïve Bayes Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Naïve Bayes algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying NB Algorithm model. The results obtained from the study using the NB model are obtained Confusion Matrix result, namely accuracy of 79,55%, Precision of 80,51%, and Sensitivity or Recall of 80,91%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.

Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector Machine

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Support Vector Machine Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Support Vector Machine algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying SVM Algorithm model. The results obtained from the study using the SVM model are obtained Confusion Matrix result, namely accuracyof 78.18%, Precision of 74.03%, and Sensitivity or Recall of 86.82%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.

Online Learning Model for Handling Different Concept Drifts Using Diverse Ensemble Classifiers on Evolving Data Streams

The rapid growth of the information technology accelerates organizations to generate vast volumes of high-velocity data streams. The concept drift is a crucial issue, and discovering the sequential patterns over data streams are more challenging. The ensemble classifiers incrementally learn the data for providing quick reaction to the concept drifts. The ensemble classifiers have to process both the gradual and sudden concept drifts that happen in the real-time data streams. Thus, a novel ensemble classifier is essential that significantly reacting to various types of concept drifts quickly and maintaining the classification accuracy. This work proposes the stream data mining on the fly using an adaptive online learning rule (SOAR) model to handle both the gradual and sudden pattern changes and improves mining accuracy. Adding the number of classifiers fails because the ensemble tends to include redundant classifiers instead of high-quality ones. Thus, the SOAR includes different diversity levels of classifiers in the ensemble to provide fast recovery from both the concept drifts. Moreover, the SOAR synthesizes the essential features of the block and online-based ensemble and updates the weight of each classifier, regarding its quality. It facilitates adaptive windowing to handle both gradual and sudden concept drifts. To reduce the computational cost and analyze the data stream quickly, the SOAR caches the occurred primitive patterns into a bitmap with the internal relationship. Finally, the experimental results show that the SOAR performs better classification and accuracy over data streams.

Feature mining simulation of video image information in multimedia learning environment based on BOW algorithm

With the development of computer image processing technology, video image information feature mining in multimedia environment has become a research hotspot in the field of education with its unique characteristics. However, the current method is to classify and retrieve through the video image information features. When the video image information is disordered, the multimedia video image information features cannot be classified, and the criterion for measuring the size of the video image information cannot be given, and the information feature mining accuracy is low. Therefore, this paper proposes a new efficient algorithm for multimedia video image information retrieval. Firstly, the SIFT features of the video image are analysed to obtain the SIFT features of the video image. Secondly, the SIFT feature is used for feature matching to identify the target image. Finally, the BOW algorithm is introduced to index the matched SIFT features, and the bag-of-words model, TF-IDF weighting and Euclidean distance are used to complete the similarity calculation of the image, and the feature mining of multimedia video image information is completed. The simulation results show that the proposed method effectively improves the feature mining speed and feature mining accuracy and has better robustness.

Research on Service Dependency Mining Technology Based on Tag Extension and Bipartite Graph

Due to the rapid development of service portfolio model on network, service computing has become a hot topic in recent years. It is important for both service management and service recommendation to clarify the dependencies between services. Tag, as a widely used network information resource in the current service field, can intuitively reflect the function, purpose and other attributes of the service, and it has become a common resource in the field of service discovery and combination. Tag sparsity, however, will reduce the accuracy of service relationship analysis. Therefore, this paper proposes a service dependency mining method based on tag extension and bipartite graph. By extending the existing tags and abstracting the combination of service and the relationship between the extension tags into a bipartite graph, dependency propagation mechanism in bipartite graph is applied to improve the comprehensiveness and accuracy of the service dependency mining. The programmable Web APIs and Mashups are used to show the validity of the service dependency mining method.