Frequent Term Sets Research Articles

Trading Strategies Model Using the Apriori Algorithm and the Neural Network Optimized by the Genetic Algorithm

A neural network trading strategy optimized based on Apriori and the genetic algorithm is proposed to determine the trading strategies. Using the Apriori algorithm and the Neural Network optimized based on the Genetic Algorithm, we find all the frequent term sets of the fuzzy set and mine the fuzzy association rules from the frequent term set. Enter the price dataset, set minimum support and multiplier; output fuzzy association rule library, and iterate.

Implementing Trading Strategies for Gold and Bchain Based on Neural Networks and Apriori Algorithms

Market traders often buy and sell volatile assets with the goal of maximizing total returns. In order to provide traders with the optimal trading strategy, this paper establishes a trading model based on neural network and Apriori algorithm. Firstly, a neural network model is established according to the given data to predict the price data on the trading day. The Apriori algorithm is then used to obtain the frequent term set, which complements the neural network model to obtain the trading strategy to determine whether to add or close a position and the size of the trade. The model is then further optimized to obtain a stable trading strategy considering the risk and cost of trading.

An intrusion detection algorithm based on data streams mining and cognitive computing

With the emergence of large-volume and high-speed streaming data, traditional techniques for mining closed frequent itemsets has become inefficient. Online mining of closed frequent itemsets over streaming data is one of the most important issues in data streams minging. In view of the low efficiency of traditional closed frequent item data mining, a combined data structure based on the principle of cognitive computing is proposed, that is, combining the effective bit first with the extended dictionary frequent item list to form a mixed data structure that can identify the closed frequent information in data streams. At the same time, a variety of pruning strategies based on cognitive computing are proposed to avoid the generation of a large number of intermediate itemsets and to remove the non closed frequent term sets from the Hash Table of Closed Itemsets (CIHT). Closed Frequent Itemset Deletion and Search Strategy (CFIDWSS) is used to effectively add or remove the closed frequent itemsets, so as to greatly reduce the search space and improve the user response speed. The proposed algorithm solves the problem of low efficiency of data streams mining of closed frequent items. On the basis of the above algorithms, this paper proposes a new intrusion detection model. Through the mining of normal or abnormal patterns of data stream information, the corresponding database of network access pattern is established. Then the database is used to detect the intrusion online in real time and improve the detection accuracy of the system. Theoretical and experimental results show that the proposed algorithm and intrusion detection system have good performance.

Word Sense Induction with Closed Frequent Termsets

The article is devoted to the problem of word sense induction. We propose a method for inducing senses from a raw text corpus. The proposed sense induction algorithm (called SenseSearcher, or SnS) is based on closed frequent sets, and as a result, it provides a multilevel sense representation. To a large extent, it is a knowledge‐poor approach, as it does not need any kind of structured knowledge base about senses and there is no deep language knowledge embedded. By discovering a hierarchy of senses, the algorithm enables identifying subsenses (fine‐grained senses). SnS discovers not only frequent (dominating) senses but also infrequent ones (dominated). The method was evaluated in two main areas: lexicography and information retrieval. With the use of the SnS algorithm, we provide a tool able to induce from a textual corpus a structure of senses, with a varying number of granularity levels. In the area of information retrieval, SnS can be used for clustering search result, according to the discovered senses. The experiments have shown that SnS performs better than the methods participating in the SemEval2013 WSI Task 11 competition, and most of the known search result clustering methods.

A computational model for knowledge extraction in uncertain textual data using karnaugh map technique

The present technology such as privacy-preserving data mining generates data, which is inherently uncertain in nature. There are other existing tools, which are also collecting data in an imprecise way. Further mining frequent patterns from uncertain textual data is not as simple as in precise data, and normal approaches that work for precise data are not applicable for uncertain data. This paper describes the motivation behind proposed method based on review of existing frequent termset mining techniques in document data. Further, a new mining method using karnaugh map is proposed for finding frequent termset from uncertain textual data, and experiment carried out requires only a single database scan for mining frequent patterns, which reduces to low processing time.

Karnaugh Map Approach for Mining Frequent Termset from Uncertain Textual Data

In recent years, uncertain textual data has become ubiquitous because of latest technologies of data collect. As the existing technologies can only collect data in an imprecise way. So in the given paper, we propose frequent pattern mining technique for mining termsets from uncertain textual data. in the said technique, we have conducted a study on uncertain textual data with Karnaugh Map concept. In fact the contribution of given paper is three manifolds. Firstly, we review existing method of finding frequent termset form document data. Secondly a new method UTDKM (Uncertain Textual Data using Karnaugh Map) is proposed for finding frequent termset from uncertain textual data, and lastly, we carried experiments to evaluate the performance of proposed method. The experimental results demonstrate that UTDKM method is better for mining frequent termset in comparison to existing methods. 

Feature Extension for Short Text Categorization Using Frequent Term Sets

A short text feature extension method based on frequent term sets is proposed to overcome the drawbacks of the vector space model (VSM) on representing short text content. After defining the co-occurring and class orientation relations between terms, frequent term sets with identical class orientation are generated by calculating the support and confidence of word sets, and then taken as the background knowledge for short text feature extension. For each single term of the short text, the term sets containing this term are retrieved in the background knowledge and added into the original term vector as the feature extension. The experimental results on Sougou corpus show that the support and confidence have great impact on the scale of the background knowledge, but excessive extension also has redundancy and cannot obtain further improvement. The background knowledge based on frequent term sets is an effective way for feature extension. When the number of the training documents is limited, these extended features can greatly improve the classification results of SVM.

Clustering Web Documents based on Efficient Multi-Tire Hashing Algorithm for Mining Frequent Termsets

Document Clustering is one of the main themes in text mining. It refers to the process of grouping documents with similar contents or topics into clusters to improve both availability and reliability of text mining applications. Some of the recent algorithms address the problem of high dimensionality of the text by using frequent termsets for clustering. Although the drawbacks of the Apriori algorithm, it still the basic algorithm for mining frequent termsets. This paper presents an approach for Clustering Web Documents based on Hashing algorithm for mining Frequent Termsets (CWDHFT). It introduces an efficient Multi-Tire Hashing algorithm for mining Frequent Termsets (MTHFT) instead of Apriori algorithm. The algorithm uses new methodology for generating frequent termsets by building the multi-tire hash table during the scanning process of documents only one time. To avoid hash collision, Multi Tire technique is utilized in this proposed hashing algorithm. Based on the generated frequent termset the documents are partitioned and the clustering occurs by grouping the partitions through the descriptive keywords. By using MTHFT algorithm, the scanning cost and computational cost is improved moreover the performance is considerably increased and increase up the clustering process. The CWDHFT approach improved accuracy, scalability and efficiency when compared with existing clustering algorithms like Bisecting K-means and FIHC.

Investigate the Performance of Document Clustering Approach Based on Association Rules Mining

The challenges of the standard clustering methods and the weaknesses of Apriori algorithm in frequent termset clustering formulate the goal of our research. Based on Association Rules mining, an efficient approach for Web Document Clustering (ARWDC) has been devised. An efficient Multi-Tire Hashing Frequent Termsets algorithm (MTHFT) has been used to improve the efficiency of mining association rules by targeting improvement in mining of frequent termset. Then, the documents are initially partitioned based on association rules. Since a document usually contains more than one frequent termset, the same document may appear in multiple initial partitions, i.e., initial partitions are overlapping. After making partitions disjoint, the documents are grouped within the partition using descriptive keywords, the resultant clusters are obtained effectively. In this paper, we have presented an extensive analysis of the ARWDC approach for different sizes of Reuter's datasets. Furthermore the performance of our approach is evaluated with the help of evaluation measures such as, Precision, Recall and F-measure compared to the existing clustering algorithms like Bisecting K-means and FIHC. The experimental results show that the efficiency, scalability and accuracy of the ARWDC approach has been improved significantly for Reuters datasets. The internet has become the largest data repository, facing the problem of information overload. The existence of an abundance of information, in combination with the dynamic and heterogeneous nature of the Web, makes information retrieval a tedious process for the average user. Search engines, Meta-Search engines and Web Directories have been developed in order to help the users quickly and easily satisfy their information need. The Search engine performs exact matching between the query terms and the keywords that characterize each web page and presents the results to the user. These results are long lists of URLs, which are very hard to search. Furthermore, users without domain expertise are not familiar with the appropriate terminology thus not submitting the right query terms, leading to the retrieval of more irrelevant pages. This has led to the need for the development of new techniques to assist users effectively navigate, trace and organize the available web documents, with the ultimate goal of finding those best matching their needs. Document Clustering is one of the techniques that can play an important role towards the achievement of this objective. Document clustering has become an increasingly important task in analyzing huge numbers of documents distributed among various sites. Furthermore organizing them into different groups called as clusters, where the documents in each cluster share some common properties according to defined similarity measure. The fast and high-quality document clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Document clustering has been studied intensively because of its wide applicability in areas such as Web Mining, Search Engines, Information Retrieval, and Topological Analysis. Document Clustering is different than document classification. In document classification, the classes (and their properties) are known a priori, and documents are assigned to these classes; whereas, in document clustering, the number, properties, or membership (composition) of classes is not known in advance. Thus, classification is an example of supervised machine learning and clustering that of

WEB DOCUMENT SEGMENTATION USING FREQUENT TERM SETS FOR SUMMARIZATION

Query sensitive summarization aims at extracting th e query relevant contents from web documents. Web page segmentation focuses on reducing the run time overhead of the summarization systems by grouping the related contents of a web page into segments. A t query time, query relevant segments of the web pa ge are identified and important sentences from these s egments are extracted to compose the summary. DOM tree structures of the web documents are utilized t o perform the segmentation of the contents. Leaf no des of DOM tress are merged to form segments according to the statistical and linguistic similarity measur e. The proposed system has been evaluated by intrinsic approach making use of user satisfaction index. Th e performance of the system is compared with summarization without using preprocessed segments. Performance of this system is more promising than t he other measures like cosine similarity, jaccard measure which make use of sparse term-frequent vectors, since the most frequent term sets are consider ed to measure the relevance. Relevant segments alone n eed to be processed at run time for summarization which reduces the time complexity of the summarization process.

Multi-objective frequent termset clustering

Large media collections rapidly evolve in the World Wide Web. In addition to the targeted retrieval as is performed by search engines, browsing and explorative navigation is an important issue. Since the collections grow fast and authors most often do not annotate their web pages according to a given ontology, automatic structuring is in demand as a prerequisite for any pleasant human–computer interface. In this paper, we investigate the problem of finding alternative high-quality structures for navigation in a large collection of high-dimensional data. We express desired properties of frequent termset clustering (FTS) in terms of objective functions. In general, these functions are conflicting. This leads to the formulation of FTS clustering as a multi-objective optimization problem. The optimization is solved by a genetic algorithm. The result is a set of Pareto-optimal solutions. Users may choose their favorite type of a structure for their navigation through a collection or explore the different views given by the different optimal solutions. We explore the capability of the new approach to produce structures that are well suited for browsing on a social bookmarking data set.

Multi-Instance Learning Based Web Mining

In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. In this paper, a web mining problem, i.e. web index recommendation, is investigated from a multi-instance view. In detail, each web index page is regarded as a bag, while each of its linked pages is regarded as an instance. A user favoring an index page means that he or she is interested in at least one page linked by the index. Based on the browsing history of the user, recommendation could be provided for unseen index pages. An algorithm named Fretcit-kNN, which employs the Minimal Hausdorff distance between frequent term sets and utilizes both the references and citers of an unseen bag in determining its label, is proposed to solve the problem. Experiments show that in average the recommendation accuracy of Fretcit-kNN is 81.0% with 71.7% recall and 70.9% precision, which is significantly better than the best algorithm that does not consider the specific characteristics of multi-instance learning, whose performance is 76.3% accuracy with 63.4% recall and 66.1% precision.