Fuzzy Itemsets Research Articles

Fuzzy based optimized itemset mining in high dimensional transactional database using adaptable FCM

One of the extremely deliberated data mining processes is HUIM (High Utility Itemset Mining). Its applications include text mining, e-learning bioinformatics, product recommendation, online click stream analysis, and market basket analysis. Likewise lot of potential applications availed in the HUIM. However, HUIM techniques could find erroneous patterns because they don’t look at the correlation of the retrieved patterns. Numerous approaches for mining related HUIs have been presented as an outcome. The computational expense of these methods continues to be problematic, both in terms of time and memory utilization. A technique for extracting weighted temporal designs is therefore suggested to rectify the identified issue in HUIM. Preprocessing of time series-based information into fuzzy item sets is the first step of the suggested technique. These feed the Graph Based Ant Colony Optimization (GACO) and Fuzzy C Means (FCM) clustering methodologies used in the Improvised Adaptable FCM (IAFCM) method. The suggested IAFCM technique achieves two objectives: optimal item placement in clusters using GACO; and ii) IAFCM clustering and information decrease in FCM cluster. The proposed technique yields high-quality clusters by GACO. Weighted sequential pattern mining, which considers facts of patterns with the highest weight and low frequency in a repository that is updated over a period, is used to locate the sequential patterns in these clusters. The outcomes of this methodology make evident that the IAFCM with GACO improves execution time when compared to other conventional approaches. Additionally, it enhances information representation by enhancing accuracy while using a smaller amount of memory.

PARALLEL FUZZY FREQUENT ITEMSET MINING USING CELLULAR AUTOMATA

Finding frequent fuzzy itemsets in operational quantitative databases is a significant challenge for fuzzy association rule mining in the context of data mining. If frequent fuzzy itemsets are detected, the decision-making process and formulating strategies in businesses will be made more precise. Because the characteristic of these data models is a large number of transactions and unlimited and high-speed productions. This leads to limitations in calculating the support for itemsets containing fuzzy attributes. As a result, mining using parallel processing techniques has emerged as a potential solution to the issue of slow availability. This study presents a reinforced technique for mining frequent fuzzy sets based on cellular learning automata (CLA). The results demonstrate that frequent set mining can be accomplished with less running time when the proposed method is compared to iMFFP and NPSFF methods.

Prediction of Cognitive-Behavioral Therapy using Deep Learning for the Treatment of Adolescent Social Anxiety and Mental Health Conditions

Cognitive-behavioral therapy is a type of psychosocial intervention which aims to reduce the mental health conditions like depression, anxiety disorder, and others. Similarly, deep learning is a type of machine learning and artificial intelligence that imitates how humans gain certain types of knowledge. In this paper, deep learning has been used to effectively alleviate teenagers’ social anxiety and improve their social ability and the quality of social relations. It aims to conduct an in-depth study on the diagnosis and treatment of cognitive behavior therapy in teenagers based on deep learning. First, it constructs the cognitive behavior diagnosis and treatment evaluation system of adolescent social anxiety and divides the system function into functional, structural, and database design. Then, the correlation prediction model between cognitive behavior therapy and adolescent social anxiety is constructed based on a multiobjective evolutionary algorithm. The risk and protective factors in adolescent growth are screened from the perspectives of people, family, school, and society. The fuzzy itemset support of different factors is defined. The vector of adolescent social anxiety expression index’s weight is calculated. The subjective and objective factors of social anxiety in adolescents are extracted based on the grey correlation degree. The correlation prediction model between accurate cognitive behavior therapy and adolescents’ social anxiety is established to complete the prediction research, diagnosis, and treatment effect. Simulation experiments show that the proposed method has good feasibility and high prediction accuracy. It can effectively alleviate the social anxiety of teenagers.

Mining fuzzy high average-utility itemsets using fuzzy utility lists and efficient pruning approach

Mining fuzzy high average-utility itemsets using fuzzy utility lists and efficient pruning approach

FIMA: A New Fuzzy Item sets Miner Algorithm

Frequent itemsets mining is the second step of Association rules mining which is the main task of Knowledge Discovery (KDD) and data mining (DM). There are three types of itemsets; Crisp (CI), Generalized (GI), and Fuzzy itemsets (FI). FI is the most recent type. This paper presents a new algorithm to mine fuzzy itemsets from large taxonomic databases depending on fuzzy taxonomies that reflect partial belongings among data items, also it depends on Item-Transaction layout, and shortest path finding between an item and its super classes. The proposed algorithm, Fuzzy Itemsets Miner Algorithm (FIMA) deals with the three types of fuzzy itemsets; taxonomic nodes, linguistic terms, and hedges. FIMA scans the database, under mining, only once. It excludes the need for complicated data structures, prunes the pruning steps of available algorithm, and avoids the weakness of manipulating low levels values of minimum support threshold. The algorithm performs much better than the available algorithm such that it reduces the complexity of mining FIs from exponential, O(an), to linear order of magnitude O(n).

EA-HUFIM: Optimization for Fuzzy-Based High-Utility Itemsets Mining

High-utility itemsets mining (HUIM) has attracted considerable attention due to its superior performance in discovering the profitable itemsets from databases. Generally, an itemset is discovered as a high-utility itemset (HUI) by HUIM only if its utility (such as profit) is higher than a predefined utility threshold. The previous HUIM methods cannot address the sharp boundary problem and the efficiency problem simultaneously. To tackle the above problems, we take the attempt to combine the fuzzy set theory and the evolutionary algorithms (EAs) in the HUIM method, which is called EA-HUFIM. For the proposed EA-HUFIM, it is challenging to design an appropriate EA and a suitable fitness function. Therefore, we focus on analyzing the characteristics of fuzzy-based HUI (i.e., HUFI) and present a theorem and a lemma based on a new concept, the high database-max utility fuzzy itemsets (HDFIs). The fitness function adopted in the EA-HUFIM method is inspired by the proposed lemma. Besides, we present a general EA framework with considering the elite learning strategy. The top K 1-HUFIs and top K 1-HDFIs are stored as an elite archive for further offspring production. To improve efficiency, we further employ a fuzzy map and a transaction map to propose a novel preprocessing method, and thus, the computational costs can be reduced. Experiments on several datasets and the results demonstrate that the EA-HUFIM method is effective in quantitative databases.

EARC: Evidential association rule-based classification

As an extension of classical fuzzy rule-based classification, the belief rule-based classification is a promising technique for handling hybrid information with multiple uncertainties in real-world applications. However, the antecedent structure of each resultant rule is fixed and hence may cause overfitting in small instance cases, while some resultant rules are also redundant due to the similarity of neighboring rules. Here, an evidential association rule-based classification method, called EARC, is developed by integrating evidential association rule mining and classification to obtain an accurate and compact classification model. First, new measures of evidential support and confidence are proposed to represent rule interestingness. Then, a three-stage rule mining algorithm is developed to generate a set of evidential classification association rules, including Apriori-based frequent fuzzy itemsets searching for discovering all possible antecedents, evidential consequents deriving in the belief function framework, and reliable rule extracting with measures of evidential support and confidence. Further, to make the classification efficient, the procedures of rule prescreening and rule selection are presented for deleting redundant rules and obtaining an accurate classifier, respectively. At last, an improved belief reasoning process is presented for classifying each input instance by combining the top K activated rules. Experimental results based on real-world datasets demonstrate the superiority of the proposed method on classification accuracy and interpretability.

Finding Fuzzy Close Frequent Itemsets from Databases

In this paper, we define the problem of fuzzy close frequent itemset mining to discover the rules of the data. A concise tree-based data synoposis named FCTree is built, where the fuzzy itemsets are sorted by their supports. In addition, an algorithm called FCFIMiner is proposed to construct and maintain the FCTree. We conduct superset pruning from the result of the FCTree. The experimental works over 2 databases show the proposed algorithm has a much better performance.

Efficient mining product-based fuzzy association rules through central limit theorem

In this study, we propose a fast algorithm to form product-based fuzzy association rules from large quantitative dataset, which reduces data size and ensures the quality of the obtained results. A method is designed to transform mining of fuzzy association rules to the binary counterpart. It is shown that the final results are not affected by this transformation. Then, an efficient sampling method is developed, where a sample is taken to replace the original large dataset, so the size of the dataset is reduced and the cost of scanning is also decreased. Through the central limit theorem, the size of sample can be set reasonably, so the deviation of support of any fuzzy itemset caused by sampling is limited in a small range with a high probability. Through a series of experiments, we show the advantages of the approach both the speed of the proposed algorithm and its reliability.

A Novel Approach in Mining Specialized Coherent Rules in a Level-Crossing Hierarchy

Multi-level association rule mining techniques that are developed for mining data at multiple-level taxonomy usually rely on minimum support and minimum confidence threshold for extracting frequent items at various levels. But the usage of above threshold alone does not produce meaningful and interesting rules. Moreover, the determination of minimum support and confidence is very hard in a dataset containing multiple-level taxonomy. In order to overcome the above drawbacks, coherent rule mining was proposed. The proposed approach first mines multiple-level coherent rules in a predefined taxonomy and then extracts specialized coherent rules. The rules are mentioned as specialized rules because they exist only in the bottommost level of taxonomy irrespective of their predecessor rules. The proposed algorithm works for the quantitative dataset, and a triangular membership function is used for converting the quantitative dataset at various levels into fuzzy itemset. The results obtained are compared with multi-level fuzzy association rule mining technique.

A survey of itemset mining

Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently cooccurring in databases. Because of its numerous applications in domains such as bioinformatics, text mining, product recommendation, e‐learning, and web click stream analysis, itemset mining has become a popular research area. This study provides an up‐to‐date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics are provided. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high‐utility itemset mining, rare itemset mining, fuzzy itemset mining, and uncertain itemset mining. This study also discusses research opportunities and the relationship to other popular pattern mining problems, such as sequential pattern mining, episode mining, subgraph mining, and association rule mining. Main open‐source libraries of itemset mining implementations are also briefly presented.WIREs Data Mining Knowl Discov2017, 7:e1207. doi: 10.1002/widm.1207This article is categorized under:Algorithmic Development > Association RulesTechnologies > Association Rules

An Efficient Approach for Incremental Mining Fuzzy Frequent Itemsets with FP-Tree

Keeping the generated fuzzy frequent itemsets up-to-date and discovering the new fuzzy frequent itemsets are challenging problems in dynamic databases. In this paper, the classical H-struct structure is extended to mining fuzzy frequent itemsets. The extended H-mine algorithm can use any t-norm operator to calculate the support of fuzzy itemset. The FP-tree-based structure called the Initial-FP-tree and the New-FP-tree are built to maintain the fuzzy frequent itemsets in the original database and the new inserted transactions respectively. The strategy of incremental mining of fuzzy frequent itemsets is achieved by breath-first-traversing the Initial-FP-tree and the New-FP-tree. All of the fuzzy frequent itemsets in the updated database can be obtained by traversing the Initial-FP-tree. The experiments on real datasets show that the proposed approach runs faster than the batch extended H-mine algorithm. Comparing with the existing algorithm for incremental mining fuzzy frequent itemsets, the proposed approach is superior in terms of the execution time. The memory cost of the proposed approach is lower than that of the existing algorithm when the minimum support threshold is low.

Fuzzy Association Rule Mining Based Myocardial Ischemia Diagnosis on ECG Signal

A fuzzy association rule mining based method is proposed for myocardial ischemia diagnosis on ECG signals. The proposal provides interpretable and understandable information to doctors as an assistant reference, while rule mining on fuzzy itemsets guarantees that the feature segmentation before rule extraction is feasible and effective. A set of fuzzy association rules is mined through experiments on data from the European ST-T Database, and classification results of myocardial ischemia and normal heartbeats on the test dataset using the extracted rules obtained values of 83.4%, 80.7%, and 81.4% for sensitivity, specificity, and accuracy, respectively. The proposed method aims to become a helpful tool to accelerate the diagnosis of myocardial ischemia on ECG signal, and to be expanded to other heart disease diagnosis areas such as hypertensive heart disease and arrhythmia.

CisMiner: genome-wide in-silico cis-regulatory module prediction by fuzzy itemset mining.

Eukaryotic gene control regions are known to be spread throughout non-coding DNA sequences which may appear distant from the gene promoter. Transcription factors are proteins that coordinately bind to these regions at transcription factor binding sites to regulate gene expression. Several tools allow to detect significant co-occurrences of closely located binding sites (cis-regulatory modules, CRMs). However, these tools present at least one of the following limitations: 1) scope limited to promoter or conserved regions of the genome; 2) do not allow to identify combinations involving more than two motifs; 3) require prior information about target motifs. In this work we present CisMiner, a novel methodology to detect putative CRMs by means of a fuzzy itemset mining approach able to operate at genome-wide scale. CisMiner allows to perform a blind search of CRMs without any prior information about target CRMs nor limitation in the number of motifs. CisMiner tackles the combinatorial complexity of genome-wide cis-regulatory module extraction using a natural representation of motif combinations as itemsets and applying the Top-Down Fuzzy Frequent- Pattern Tree algorithm to identify significant itemsets. Fuzzy technology allows CisMiner to better handle the imprecision and noise inherent to regulatory processes. Results obtained for a set of well-known binding sites in the S. cerevisiae genome show that our method yields highly reliable predictions. Furthermore, CisMiner was also applied to putative in-silico predicted transcription factor binding sites to identify significant combinations in S. cerevisiae and D. melanogaster, proving that our approach can be further applied genome-wide to more complex genomes. CisMiner is freely accesible at: http://genome2.ugr.es/cisminer. CisMiner can be queried for the results presented in this work and can also perform a customized cis-regulatory module prediction on a query set of transcription factor binding sites provided by the user.

Actionable high-coherent-utility fuzzy itemset mining

Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from quantitative transaction databases. Since each item has its own utility, utility itemset mining has become increasingly important. However, common problems with existing approaches are that an appropriate minimum support is difficult to determine and that the derived rules usually expose common-sense knowledge, which may not be interesting from a business point of view. This study thus proposes an algorithm for mining high-coherent-utility fuzzy itemsets to overcome problems with the properties of propositional logic. Quantitative transactions are first transformed into fuzzy sets. Then, the utility of each fuzzy itemset is calculated according to the given external utility table. If the value is larger than or equal to the minimum utility ratio, the itemset is considered as a high-utility fuzzy itemset. Finally, contingency tables are calculated and used for checking whether a high-utility fuzzy itemset satisfies four criteria. If so, it is a high-coherent-utility fuzzy itemset. Experiments on the foodmart and simulated datasets are made to show that the derived itemsets by the proposed algorithm not only can reach better profit than selling them separately, but also can provide fewer but more useful utility itemsets for decision-makers.

A Strategic Study of Mining Fuzzy Association Rules Using Fuzzy Multiple Correlation Measues

Two different data variables may behave very similarly. Correlation is the problem of determining how much alike the two variables actually are and association rules are used just to show the relationships between data items. Mining fuzzy association rules is the job of finding the fuzzy item-sets which frequently occur together in large fuzzy data set, where the presence of one fuzzy item-set in a record does not necessarily imply the presence of the other one in the same record. In this paper a new method of discovering fuzzy association rules using fuzzy correlation rules is proposed, because the fuzzy support and confidence measures are insufficient at filtering out uninteresting fuzzy correlation rules. To tackle this weakness, a fuzzy correlation measure for fuzzy numbers, is used to augment the fuzzy support-confidence framework for fuzzy association rules. We have extended the Apriori algorithm to fuzzy multiple correlation analysis, which is the new approach presented in this paper comparing to most of the previous works. A practical study over the academic behaviour of a particular school is done and some valuable suggestions are given, based on the results obtained.

A study on a novel method of mining fuzzy association using fuzzy correlation analysis

Two different data variables may behave very similarly. Correlation is the problem of determining how much alike the two variables actually are and association rules are used just to show the relationships between data items. Mining fuzzy association rules is the job of finding the fuzzy item-sets which frequently occur together in large fuzzy data set, where the presence of one fuzzy item-set in a record does not necessarily imply the presence of the other one in the same record. In this paper a new method of discovering fuzzy association rules using fuzzy correlation rules is proposed, because the fuzzy support and confidence measures are insufficient at filtering out uninteresting fuzzy correlation rules. To tackle this weakness, a fuzzy correlation measure for fuzzy numbers, is used to augment the fuzzy support-confidence framework for fuzzy association rules. A practical study over the academic behaviour of a particular school is done and some valuable suggestions are given, based on the results obtained. Key words: Fuzzy association rules, fuzzy item-sets, fuzzy data sets, fuzzy support-confidence, fuzzy correlation measure.

Applying cluster-based fuzzy association rules mining framework into EC environment

During electronic commerce (EC) environment, how to effectively mine the useful transaction information will be an important issue to be addressed in designing the marketing strategy for most enterprises. Especially, the relationships between different databases (e.g., the transaction and online browsing database) may have the unknown and potential knowledge of business intelligence. Two important issues of mining association rules were mentioned to address EC application in this study. The first issue is the discovery of generalized fuzzy association rules in the transaction database. The second issue is to discover association rules from the web usage data and the large itemsets identified in the transaction database. A cluster-based fuzzy association rules (CBFAR) mining architecture is then proposed to simultaneously address such two issues in this study. Three contributions were achieved as: (a) an efficient fuzzy association rule miner based on cluster-based fuzzy-sets tables is presented to identify all the large fuzzy itemsets; (b) this approach requires less contrast to generate large itemsets; (3) a fuzzy rule mining approach is used to compute the confidence values for discovering the relationships between transaction database and browsing information database. Finally, a simulated example during EC environment is provided to demonstrate the rationality and feasibility of the proposed approach.

New algorithms for finding approximate frequent item sets

In standard frequent item set mining a transaction supports an item set only if all items in the set are present. However, in many cases this is too strict a requirement that can render it impossible to find certain relevant groups of items. By relaxing the support definition, allowing for some items of a given set to be missing from a transaction, this drawback can be amended. The resulting item sets have been called approximate, fault-tolerant or fuzzy item sets. In this paper we present two new algorithms to find such item sets: the first is an extension of item set mining based on cover similarities and computes and evaluates the subset size occurrence distribution with a scheme that is related to the Eclat algorithm. The second employs a clustering-like approach, in which the distances are derived from the item covers with distance measures for sets or binary vectors and which is initialized with a one-dimensional Sammon projection of the distance matrix. We demonstrate the benefits of our algorithms by applying them to a concept detection task on the 2008/2009 Wikipedia Selection for schools and to the neurobiological task of detecting neuron ensembles in (simulated) parallel spike trains.

Fuzzy data mining for time-series data

Time series analysis has always been an important and interesting research field due to its frequent appearance in different applications. In the past, many approaches based on regression, neural networks and other mathematical models were proposed to analyze the time series. In this paper, we attempt to use the data mining technique to analyze time series. Many previous studies on data mining have focused on handling binary-valued data. Time series data, however, are usually quantitative values. We thus extend our previous fuzzy mining approach for handling time-series data to find linguistic association rules. The proposed approach first uses a sliding window to generate continues subsequences from a given time series and then analyzes the fuzzy itemsets from these subsequences. Appropriate post-processing is then performed to remove redundant patterns. Experiments are also made to show the performance of the proposed mining algorithm. Since the final results are represented by linguistic rules, they will be friendlier to human than quantitative representation.