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With recent advancements in network technology and the increasing popularity of the internet, the use of social network services and Internet of Things devices has flourished, leading to a continuous generation of large volumes of graph stream data, where changes, such as additions or deletions of vertices and edges, occur over time. Additionally, owing to the need for the efficient use of storage space and security requirements, graph stream data compression has become essential in various applications. Even though various studies on graph compression methods have been conducted, most of them do not fully reflect the dynamic characteristics of graph streams and the complexity of large graphs. In this paper, we propose a compression scheme using provenance data to efficiently process and analyze large graph stream data. It obtains provenance data by analyzing graph stream data and builds a pattern dictionary based on this to perform dictionary-based compression. By improving the existing dictionary-based graph compression methods, it enables more efficient dictionary management through tracking pattern changes and evaluating their importance using provenance. Furthermore, it considers the relationships among sub-patterns using an FP-tree and performs pattern dictionary management that updates pattern scores based on time. Our experiments show that the proposed scheme outperforms existing graph compression methods in key performance metrics, such as compression rate and processing time. [ABSTRACT FROM AUTHOR]

Data mining is one of the emerging technologies used in many applications such as Market analysis and Machine learning. Temporal data mining is used to get a clear knowledge about current trend and to predict the upcoming future. The rudimentary challenge in introducing a data mining procedure is, processing time and memory consumption are highly increasing while trying to improve the accuracy, precision or recall. As well as, while trying to reduce the processing time or memory consumption, accuracy, precision and recall values are reducing significantly. So, for improving the performance of the system and to preserve the memory and processing time, Three-Dimensional Fuzzy FP-Tree (TDFFPT) is proposed for Temporal data mining. Three functional modules namely, Three-Dimensional Temporal data FP-Tree (TTDFPT), Fuzzy Logic based Temporal Data Tree Analyzer (FTDTA) and Temporal Data Frequent Itemset Miner (TDFIM) are integrated in the proposed method. This algorithm scans the database and generates frequent patterns as per the business need. Every time a client purchases a new item, it gets stored in the recent database layer instead of rescanning the entire records which are placed in the old layer. The results obtained shows that the performance of the proposed model is more efficient than that of the existing algorithm in terms of overall accuracy, processing time, reduction in the memory utilization, and the number of databases scans. In addition, the proposed model also provides improved decision making and accurate pattern prediction in the time series data. [ABSTRACT FROM AUTHOR]

With recent advancements in network technology and the increasing popularity of the internet, the use of social network services and Internet of Things devices has flourished, leading to a continuous generation of large volumes of graph stream data, where changes, such as additions or deletions of vertices and edges, occur over time. Additionally, owing to the need for the efficient use of storage space and security requirements, graph stream data compression has become essential in various applications. Even though various studies on graph compression methods have been conducted, most of them do not fully reflect the dynamic characteristics of graph streams and the complexity of large graphs. In this paper, we propose a compression scheme using provenance data to efficiently process and analyze large graph stream data. It obtains provenance data by analyzing graph stream data and builds a pattern dictionary based on this to perform dictionary-based compression. By improving the existing dictionary-based graph compression methods, it enables more efficient dictionary management through tracking pattern changes and evaluating their importance using provenance. Furthermore, it considers the relationships among sub-patterns using an FP-tree and performs pattern dictionary management that updates pattern scores based on time. Our experiments show that the proposed scheme outperforms existing graph compression methods in key performance metrics, such as compression rate and processing time.

Frequent itemsets are itemsets that occur frequently in a dataset. Frequent itemset mining extracts specific itemsets with supports higher than or equal to a minimum support threshold. Many mining methods have been proposed but Apriori and FP-growth are still regarded as two prominent algorithms. The performance of the frequent itemset mining depends on many factors; one of them is searching the nodes while constructing the tree. This paper introduces a new prefix-tree structure called child structured frequent pattern tree (CSFP-tree), an FP-tree attached with a child search subtree to each node. The experimental results reveal that the CSFP-tree is superior to the FP-tree and its new variations for any kind of datasets. [ABSTRACT FROM AUTHOR]

Predicting transportation mode choice is a critical component of forecasting travel demand. Recently, machine learning methods have become increasingly more popular in predicting transportation mode choice. Class association rules (CARs) have been applied to transportation mode choice, but the application of the imputed rules for prediction remains a long-standing challenge. Based on CARs, this paper proposes a new rule merging approach, called CARM, to improve predictive accuracy. In the suggested approach, first, CARs are imputed from the frequent pattern tree (FP-tree) based on the frequent pattern growth (FP-growth) algorithm. Next, the rules are pruned based on the concept of pessimistic error rate. Finally, the rules are merged to form new rules without increasing predictive error. Using the 2015 Dutch National Travel Survey, the performance of suggested model is compared with the performance of CARIG that uses the information gain statistic to generate new rules, class-based association rules (CBA), decision trees (DT) and the multinomial logit (MNL) model. In addition, the proposed model is assessed using a ten-fold cross validation test. The results show that the accuracy of the proposed model is 91.1%, which outperforms CARIG, CBA, DT and the MNL model. [ABSTRACT FROM AUTHOR]

To discover a novel and dynamic approach for frequent itemsets generation and also for generating association rules is an imperative aspect in data mining. With the fast increase in databases, new transactions added, the incremental mining is acquainted to resolve the issues of maintaining association rules in updated databases. Earlier algorithms focused on this problem which consumed more time and costly to mine and may lack at generating qualitative rules. This paper intends to analyze the tree construction like Frequent Pattern-tree (FP), Preorder Coded (POC) tree and PrePostCoded (PPC) tree for sinking overheads and time constraints. To overcome the issue of updating association rules when new transactions addition this paper proposes a dynamic frequent itemsets mining approach using Incremental Preorder Coded (IPOC) tree. The results were compared with existing ones by time, space and certainty factor (CF) of rues which were generated. This will reduce computational and scanning overheads of original dataset against addition of new transaction items and also works in an optimized way. An analysis was done on existing algorithms and compares time complexities for various standard datasets. The proposed approach shown better performance against existed ones over time and efficiency. [ABSTRACT FROM AUTHOR]

Extraction of frequent itemsets is a key step in association rule mining. Frequent Pattern (FP) mining from a very large dataset is still a challenging research problem. The basic frequent itemset algorithms are Apriori and FP-growth. FP-growth uses Frequent Pattern Tree (FP-tree) to store the database information in a compressed form. A large number of research papers have been proposed as an improvement of the basic frequent itemset mining algorithms. Several researchers have proposed modifications to existing data structures as well as new data structures to improve the mining process. A new method, Size Reduced Mining (SR-Mine), is proposed to speed up the FP-tree creation. The proposed work is implemented with the basic FP-growth algorithm and with the other two recent algorithms based on FP-tree. The three modified algorithms have been tested with standard datasets and compared with the original algorithms. The proposed method can be applied with the frequent itemset mining algorithms which consider each transaction one by one to construct a data structure for mining. The experimental results show that the proposed method can improve the performance of the mining. [ABSTRACT FROM AUTHOR]

A method was preposed for mining conditional preferences and learning CP-nets on the data stream based on the inverted matrix structure. The transaction layout using the inverted matrix reduces the number of times the database is scanned, and through random access, frequent preference items can be found in less than one full scan. In addition, through the establishment of frequent preference tree FP-Tree, the generation of candidates is reduced. Experimental results show that, compared with other methods of learning CP-nets structure, this method can obtain accurate CP-nets faster, show better performance in large transaction databases, and reduce memory requirements.

It is very difficult to generate stories in Chinese language. So far, there is no effective method to generate smooth articles. Here proposed a novel approach to improve the generation of Chinese stories in artificial intelligence, in order that it can effectively control the part-of-speech structure in sentence generation to imitate the writer's writing style. The main proposal consists of three parts. First, the pre-processing of the sentence discards the input as the summary and the output as the text. It uses the format containing < SOS > < MOS > < EOS > for processing and the detailed method is defined in session 4. The second part is for vectorization. Traditional vectorization methods include Word2vec, Fasttext, LexVec and Glove; the different vectorization methods can help data semantic or grammatical understanding. Combining different vectorization methods improves the information of the input data. Therefore, this paper proposes the multi-channel word embedding and the details defined in session 5. The last part contains the optimization of the model architecture and how to control the process of sentence generation effectively. It also rewrites the Bert model proposed by Google to be the proposed model architecture. In addition, the Softmax function had been optimizing to reduce the search time during training and increase the training speed in the model. To make the model have better performance, the necessary training of the generative adversarial network was carried out, and the GAN architecture was revised for the data set, and the detail is defined in session 6. After the model is trained, to effectively control the structure of the generated sentence. This paper proposes a complete generation flowchart. In the process, based on the concept of FP-Tree, all sentences in the data set are built into a tree structure, and the part-of-speech structure of the next sentence is restricted through model generation combined with FP-Tree and the detail is defined in session 7. In addition, the experimental results show that our proposed method can effectively control the results of Chinese story generation and generate sentences with better performance and the detail is defined in session 8. [ABSTRACT FROM AUTHOR]

One of the most challenging tasks in association rule mining is that when a new incremental database is added to an original database, some existing frequent itemsets may become infrequent itemsets and vice versa. As a result, some previous association rules may become invalid and some new association rules may emerge. We designed a new, more efficient approach for incremental association rule mining using a Fast Incremental Updating Frequent Pattern growth algorithm (FIUFP-Growth), a new Incremental Conditional Pattern tree (ICP-tree), and a compact sub-tree suitable for incremental mining of frequent itemsets. This algorithm retrieves previous frequent itemsets that have already been mined from the original database and their support counts then use them to efficiently mine frequent itemsets from the updated database and ICP-tree, reducing the number of rescans of the original database. Our algorithm reduced usages of resource and time for unnecessary sub-tree construction compared to individual FP- Growth, FUFP-tree maintenance, Pre-FUFP, and FCFPIM algorithms. From the results, at 3% minimum support threshold, the average execution time for pattern growth mining of our algorithm performs 46% faster than FP- Growth, FUFP-tree, Pre-FUFP, and FCFPIM. This approach to incremental association rule mining and our experimental findings may directly benefit designers and developers of computer business intelligence methods.

We consider the issues of data analysis with items from the Cartesian product of finite partially ordered sets. For efficiently mining frequent items generated by all possible binarization options for the original nonbinary data, we use a modification of the classic FP-tree (Frequent Pattern Tree). Time savings are achieved through the use of parallel computing based on Compute Unified Device Architecture (CUDA) technology. The results of testing the constructed parallel procedures for synthesizing the desired frequent items using model and real data are presented. [ABSTRACT FROM AUTHOR]

Frequent itemsets mining (FIM) as well as other mining techniques has been being challenged by large scale and rapidly expanding datasets. To address this issue, we propose a solution for incremental frequent itemsets mining using a Full Compression Frequent Pattern Tree (FCFP-Tree) and related algorithms called FCFPIM. Unlike FP-tree, the FCFP-Tree maintains complete information of all the frequent and infrequent items in the original dataset. This allows the FCFPIM algorithm not to waste any scan and computational overhead for the previously processed original dataset when new dataset are added and support changes. Therefore, much processing time is saved. Importantly, FCFPIM adopts an effective tree structure adjustment strategy when the support of some items changes due to the arrival of new data. FCFPIM is conducive to speeding up the performance of incremental FIM. Although the tree structure containing the lossless items information is space-consuming, a compression strategy is used to save space. We conducted experiments to evaluate our solution, and the experimental results show the space-consuming is worthwhile to win the gain of execution efficiency, especially when the support threshold is low.

The traditional measurement method of similarity ignores the project of multi-attribute category differences. To avoid this problem, this paper proposed a recommendation algorithm for improving project multi-attribute classification. Firstly, it used the project-user membership matrix to explore the affiliation and created a similar neighbor FP-Tree to extract the nearest neighbor set. Then it analyzed the common item similarity between users and the difference of the project multi-attribute classification, and used the weight factor to combine the common project with multi-attribute classification. It constructed the CNB model to measure the similarity degree of neighbors. Finally, it sorted the similar users in descending order to obtain more accurate similar users and complete the recommendation work. In virtue of the medical dataset, it verified the effectiveness of the proposed algorithm. The experimental results show that the recommendation accuracy of time complexity and mean of average accuracy have been improved. [ABSTRACT FROM AUTHOR]

This study proposes the optimization method of the associative knowledge graph using TF-IDF based ranking scores. The proposed method calculates TF-IDF weights in all documents and generates term ranking. Based on the terms with high scores from TF-IDF based ranking, optimized transactions are generated. News data are first collected through crawling and then are converted into a corpus through preprocessing. Unnecessary data are removed through preprocessing including lowercase conversion, removal of punctuation marks and stop words. In the document term matrix, words are extracted and then transactions are generated. In the data cleaning process, the Apriori algorithm is applied to generate association rules and make a knowledge graph. To optimize the generated knowledge graph, the proposed method utilizes TF-IDF based ranking scores to remove terms with low scores and recreate transactions. Based on the result, the association rule algorithm is applied to create an optimized knowledge model. The performance is evaluated in rule generation speed and usefulness of association rules. The association rule generation speed of the proposed method is about 22 seconds faster. And the lift value of the proposed method for usefulness is about 0.43 to 2.51 higher than that of each one of conventional association rule algorithms. [ABSTRACT FROM AUTHOR]

Extracting log template is a very effective method to deal with massive system logs. In this paper, based on Web system logs, the template extraction method based on label recognition tree is used to extract log template, and on this basis, the log preprocessing and template expression generation method are studied and improved. Aiming at the complex structure problem of system logs, the preprocessing method based on text similarity is adopted, and the classification of a log message is realized. The use of the maximum template matching method solves the problem of inconsistent log format and insufficient template matching caused by word segmentation. In the end, the log template extraction method is evaluated. The results show that the accuracy of the method is up to 96.4%, and the template matching degree is greatly increased. [ABSTRACT FROM AUTHOR]

Mining association rules in large database is one of most popular data mining techniques for business decision makers. Discovering frequent item set is the core process in association rule mining. Numerous algorithms are available in the literature to find frequent patterns. Apriori and FP-tree are the most common methods for finding frequent items. Apriori finds significant frequent items using candidate generation with more number of data base scans. FP-tree uses two database scans to find significant frequent items without using candidate generation. This proposed TR-FCTM (Transaction Reduction- Frequency Count Table Method) discovers significant frequent items by generating full candidates once to form frequency count table with one database scan. Experimental results of TR-FCTM shows that this algorithm outperforms than Apriori and FP-tree.

This study proposes the optimization method of the associative knowledge graph using TF-IDF based ranking scores. The proposed method calculates TF-IDF weights in all documents and generates term ranking. Based on the terms with high scores from TF-IDF based ranking, optimized transactions are generated. News data are first collected through crawling and then are converted into a corpus through preprocessing. Unnecessary data are removed through preprocessing including lowercase conversion, removal of punctuation marks and stop words. In the document term matrix, words are extracted and then transactions are generated. In the data cleaning process, the Apriori algorithm is applied to generate association rules and make a knowledge graph. To optimize the generated knowledge graph, the proposed method utilizes TF-IDF based ranking scores to remove terms with low scores and recreate transactions. Based on the result, the association rule algorithm is applied to create an optimized knowledge model. The performance is evaluated in rule generation speed and usefulness of association rules. The association rule generation speed of the proposed method is about 22 seconds faster. And the lift value of the proposed method for usefulness is about 0.43 to 2.51 higher than that of each one of conventional association rule algorithms.