Huge Number Of Patterns Research Articles

Discovering closed and maximal embedded patterns from large tree data

Many current applications and systems produce large tree datasets and export, exchange, and represent data in tree-structured form. Extracting informative patterns from large data trees is an important research direction with multiple applications in practice. Pattern mining research initially focused on mining induced patterns and gradually evolved into mining embedded patterns. A well-known problem of frequent pattern mining is the huge number of patterns it produces. This affects not only the efficiency but also the effectiveness of mining. A typical solution to this problem is to summarize frequent patterns through closed and maximal patterns. No previous work addresses the problem of mining closed and/or maximal embedded tree patterns, not even in the framework of mining multiple small trees.We address the problem of summarizing embedded tree patterns extracted from large data trees, by defining and mining closed and maximal embedded unordered tree patterns. We design an embedded frequent pattern mining algorithm extended with a local closedness checking technique. This algorithm is called closedEmbTM-eager as it eagerly eliminates non-closed patterns. To mitigate the generation of intermediate patterns, we devise pattern search space pruning rules to proactively detect and prune branches in the pattern search space which do not correspond to closed patterns. The pruning rules are accommodated into the extended embedded pattern miner to produce a new algorithm, called closedEmbTM-prune, for mining all the closed and maximal embedded frequent patterns. Our extensive experiments on synthetic and real large-tree datasets demonstrate that, on dense datasets, closedEmbTM-prune not only generates a complete closed and maximal pattern set which is substantially smaller than that generated by the embedded pattern miner, but also runs much faster with negligible overhead on pattern pruning.

Efficient mixture control chart pattern recognition using adaptive RBF neural network

In this paper, a stochastic gradient method based adaptive version of the radial basis function neural network has proposed to map the pattern features of the control chart patterns in different categories to recognize their belonging class. Adaptiveness has given over the spreadness and centers of Gaussian basis function appeared in the hidden nodes of the radial basis function neural network. Along with normal abnormalities in patterns, the mixture of different abnormal patterns has also considered capturing the worst possible conditions of abnormalities in real time. The advantages of the proposed method have appeared as very high recognition accuracy, minimum error in learning and generalize performance with small training dataset in control chart pattern recognition. Achieved performance has compared with the state of art results available in the literature which has applied feature based recognition using Support vector machine and Genetic algorithm. The proposed method has enhanced the recognition generalization of control chart patterns with simplicity in design and high level of decision confidence. The performances have achieved through the simulation-based experiments over a huge number of patterns containing ten different types of pattern and on average, 99.99% accuracy has achieved.

Investigating TYPE constraint for frequent pattern mining

Frequent pattern mining is one of the hottest fields of research in data mining. It has proven quite valuable for a variety of pattern analyses. However, a major limitation of existing or traditional frequent pattern mining techniques is the extraction of a huge number of patterns, which complicates the analysis performed by the users. To reduce these overwhelming patterns; a refined pattern mining technique that extracts only useful and to-the-point patterns for the users, is required. Finding such patterns of user’s interest from large databases is a challenging research issue, which can be mitigated via constraint-based data mining. In this scenario, a pattern mining technique that incorporates specific interests (constraints) of the user could result in a substantial reduction in the number of patterns generated. In this paper, a similar technique has been proposed and investigated. In this regard, firstly all the well-defined and categorized constraints are surveyed. From the survey, it is concluded that an important constraint “type” is referred to in the literature but not categorized based on its characteristics. One of the contributions of this paper is categorization of this important constraint “type”. Further, in this paper, the “type” constraint is divided into four modes for the sake of characterization and algorithms to employ the “type” constraints are also proposed. Moreover, the amalgamated constraints are proposed and characterized. Finally; comparative study of the proposed work with the existing techniques is presented and it is found that the proposed technique is promising in terms of reduced computational complexity.

EFFICIENT MINING OF CLOSED TREE PATTERNS FROM LARGE TREE DATABASES WITH SUBTREE CONSTRAINT

Mining frequent tree patterns from tree databases has practical importance in domains like Web mining, Bioinformatics, and so on. Although there have been algorithms on efficient tree mining, these algorithms are often lack of the interpretability in that they often produce a huge number of patterns, most of which are meaningless to users. This paper aims at both demands, one with respect to computational cost, which is efficient generation of tree patterns, and another one with respect to the interpretability. This task requires an efficient method to incorporate the users' needs into mining process. We propose a new top-down method for mining unordered closed tree patterns from a database of trees such that every mined pattern must contain a common piece of information in the form of a tree specified by the user. This type of mining is called mining with subtree constraint which would be useful, for example, inWeb mining and Bioinformatics, where users want to extract common patterns around some given information from original data. The proposed algorithm is tested and compared with a state-of-the-art tree mining algorithm on real and artificial datasets with very good results.

Nonlinear quantization on Hebbian-type associative memories

Hebbian-type associative memory is characterized by its simple architecture. However, the hardware implementation of Hebbian-type associative memories is normally complicated when there are a huge number of patterns stored. To simplify the interconnection values of a network, a nonlinear quantization strategy is presented. The strategy takes into account the property that the interconnection values are Gaussian distributed, and divides the interconnection weight values into a small number of unequal ranges accordingly. Interconnection weight values in each range contain information equally and each range is quantized to a value. The equation of probability of direct convergence was derived. The probability of direct convergence of nonlinear quantized networks with a small number of ranges is compatible with their original networks. The effects of linear and nonlinear quantization were also assessed in terms of recall capability, information capacity, and number of bits storing interconnection values saved by quantization. The performance of the proposed nonlinear quantization strategy is better than that of the linear quantization while retaining a recall capability that is compatible with its original network. The proposed approach reduces the number of connection weights and the size of the chip areas of a Hebbian-type associative memory while approximately retaining its performance.

O3R: Ontology-based mechanism for a human-centered environment targeted at the analysis of navigation patterns

Web Usage Mining (WUM) is the application of data mining techniques over Web server logs in order to extract navigation usage patterns. The analysis of mining patterns for assessing the knowledge they reveal is a critical phase in WUM. The main challenges are: (a) mining algorithms yield a huge number of patterns and (b) there is a significant semantic gap between URLs and events performed by users. In this paper, we describe the pattern analysis mechanisms integrated in O3R (Ontology-based Rules Retrieval and Rummaging), a human-centered environment for the analysis of navigation rules. O3R explores the synergy of mechanisms for retrieving and analyzing patterns. Filtering and clustering allow users to retrieve subsets of patterns with specific characteristics, in order to deal with the large volume of patterns. Rummaging mechanisms are targeted at assessing the meaning and relevance of pattern with regard to the domain, and it is particularly suitable for exploratory analysis. The distinctive feature of O3R is that is dynamically associates meaning to patterns using the concepts and relationships of a domain ontology, as a means of reducing the gap between syntactic URLs and semantic events performed by users. The paper describes the mechanisms in detail, and explores their synergic integration in the O3R prototype. It also reports two case studies that evaluate the use of O3R for the analysis of navigation patterns of a learning site.

On Identifying Useful Patterns to Analyze Products in Retail Transaction Databases

Mining correlated patterns in large transaction databases is one of the essential tasks in data mining since a huge number of patterns are usually mined, but it is hard to find patterns with the correlation. The needed data analysis should be made according to the requirements of the particular real application. In previous mining approaches, patterns with the weak affinity are found even with a high minimum support. In this paper, we suggest weighted support affinity pattern mining in which a new measure, weighted support confidence (ws-confidence) is developed to identify correlated patterns with the weighted support affinity. To efficiently prune the weak affinity patterns, we prove that the ws-confidence measure satisfies the anti-monotone and cross weighted support properties which can be applied to eliminate patterns with dissimilar weighted support levels. Based on the two properties, we develop a weighted support affinity pattern mining algorithm (WSP). The weighted support affinity patterns can be useful to answer the comparative analysis queries such as finding itemsets containing items which give similar total selling expense levels with an acceptable error range α% and detecting item lists with similar levels of total profits. In addition, our performance study shows that WSP is efficient and scalable for mining weighted support affinity patterns.

Constraining and summarizing association rules in medical data

Association rules are a data mining technique used to discover frequent patterns in a data set. In this work, association rules are used in the medical domain, where data sets are generally high dimensional and small. The chief disadvantage about mining association rules in a high dimensional data set is the huge number of patterns that are discovered, most of which are irrelevant or redundant. Several constraints are proposed for filtering purposes, since our aim is to discover only significant association rules and accelerate the search process. A greedy algorithm is introduced to compute rule covers in order to summarize rules having the same consequent. The significance of association rules is evaluated using three metrics: support, confidence and lift. Experiments focus on discovering association rules on a real data set to predict absence or existence of heart disease. Constraints are shown to significantly reduce the number of discovered rules and improve running time. Rule covers summarize a large number of rules by producing a succinct set of rules with high-quality metrics.

Efficient Biologically-based Pattern-recognizing Networks

A biologiclly-motivated classifying neural network which is based on the feature extraction scheme found in the visual cortex is suggested. A special process is proposed for grading and automatically selecting the “best” features for specific recognition tasks. Ranking is based on a feature's calculated discriminating ability, such that a given class is separated from each and every other class by a given amount. The outcome is a net with less computational complexity than other neural nets, yet one which is more biologically plausible. The main motivation for constructing a reduced net is that the complex circuitry of the brain deals with a huge number of patterns, while a machine-based recognition system usually deals with a limited number of patterns. Results show that feature reduction is drastic and that very compact nets, of the order of tens of neurons, can be used to classify patterns, even in a noisy environment. Copyright © 1996 Elsevier Science Ltd