Evaluation and optimization of frequent association rule based classification

Deriving useful and interesting rules from a data mining system is an essential and important task. Problems such as the discovery of random and coincidental patterns or patterns with no significant values, and the generation of a large volume of rules from a database commonly occur. Works on sustaining the interestingness of rules generated by data mining algorithms are actively and constantly being examined and developed. In this paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided. Empirical results show that with a proper combination of data mining and statistical analysis, the framework is capable of eliminating a large number of non-significant, redundant and contradictive rules while preserving relatively valuable high accuracy and coverage rules when used in the classification problem. Moreover, the results reveal the important characteristics of mining frequent itemsets, and the impact of confidence measure for the classification task

Unexpected rules using a conceptual distance based on fuzzy ontology

AbstractOne of the major drawbacks of data mining methods is that they generate a notably large number of rules that are often obvious or useless or, occasionally, out of the user’s interest. To address such drawbacks, we propose in this paper an approach that detects a set of unexpected rules in a discovered association rule set. Generally speaking, the proposed approach investigates the discovered association rules using the user’s domain knowledge, which is represented by a fuzzy domain ontology. Next, we rank the discovered rules according to the conceptual distances of the rules

Image Mining for Flower Classification by Genetic Association Rule Mining Using GLCM features

Image mining is concerned with knowledge discovery in image databases. It is the extension of data mining algorithms to image processing domain. Image mining plays a vital role in extracting useful information from images. In computer aided plant identification and classification system the image mining will take a crucial role for the flower classification. The content image based on the low-level features such as color and textures are used to flower image classification. A flower image is segmented using a histogram threshold based method. The data set has different flower species with similar appearance (small inter class variations) across different classes and varying appearance (large intra class variations) within a class. Also the images of flowers are of different pose with cluttered background under varying lighting conditions and climatic conditions. The flower images were collected from World Wide Web in addition to the photographs taken up in a natural scene. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM). This paper introduces multi dimensional genetic association rule mining for classification of flowers effectively. The image Data mining approach has four major steps: Preprocessing, Feature Extraction, Preparation of Transactional database and multi dimensional genetic association rule mining and classification. The purpose of our experiments is to explore the feasibility of data mining approach. Results will show that there is promise in image mining based on multi dimensional genetic association rule mining. It is well known that data mining techniques are more suitable to larger databases than the one used for these preliminary tests. Computer-aided method using association rule could assist people and improve the accuracy of flower identification. In particular, a Computer aided method based on association rules becomes more accurate with a larger dataset .Experimental results show that this new method can quickly and effectively mine potential association rules

Towards a theory unifying implicative interestingness measures and critical values consideration in MGK

The present paper shows the possibility and the benefit to compute statistical freshold for the so-called Guillaume-Kenchaff interestingness measure MGK of association rule and compares it with other measures as Confidence, Lift and Lovinger’s one. Afterwards, it proposes a theory of normalized interestingness measure unifying a set of rule quality measures in a binary context and being surprisingly centered on MGK

Discriminative and informative subspace assessment with categorical and numerical outcomes

CEECIND/01399/2017Pattern discovery and subspace clustering play a central role in the biological domain, supporting for instance putative regulatory module discovery from omics data for both descriptive and predictive ends. In the presence of target variables (e.g. phenotypes), regulatory patterns should further satisfy delineate discriminative power properties, well-established in the presence of categorical outcomes, yet largely disregarded for numerical outcomes, such as risk profiles and quantitative phenotypes. DISA (Discriminative and Informative Subspace Assessment), a Python software package, is proposed to evaluate patterns in the presence of numerical outcomes using well-established measures together with a novel principle able to statistically assess the correlation gain of the subspace against the overall space. Results confirm the possibility to soundly extend discriminative criteria towards numerical outcomes without the drawbacks well-associated with discretization procedures. Results from four case studies confirm the validity and relevance of the proposed methods, further unveiling critical directions for research on biotechnology and biomedicine. Availability: DISA is freely available at https://github.com/JupitersMight/DISA under the MIT license.publishersversionpublishe

Semantically-guided evolutionary knowledge discovery from texts

This thesis proposes a new approach for structured knowledge discovery from texts which considers both the mining process itself, the evaluation of this knowledge by the model, and the human assessment of the quality of the outcome.This is achieved by integrating Natural-Language technology and Genetic Algorithms to produce explanatory novel hypotheses. Natural-Language techniques are specifically used to extract genre-based information from text documents. Additional semantic and rhetorical information for generating training data and for feeding a semistructured Latent Semantic Analysis process is also captured.The discovery process is modeled by a semantically-guided Genetic Algorithm which uses training data to guide the search and optimization process. A number of novel criteria to evaluate the quality of the new knowledge are proposed. Consequently, new genetic operations suitable for text mining are designed, and techniques for Evolutionary Multi-Objective Optimization are adapted for the model to trade off between different criteria in the hypotheses.Domain experts were used in an experiment to assess the quality of the hypotheses produced by the model so as to establish their effectiveness in terms of novel and interesting knowledge. The assessment showed encouraging results for the discovered knowledge and for the correlation between the model and the human opinions

Flexible constrained sampling with guarantees for pattern mining

Pattern sampling has been proposed as a potential solution to the infamous pattern explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns are sampled proportional to a given quality measure. Several sampling algorithms have been proposed, but each of them has its limitations when it comes to 1) flexibility in terms of quality measures and constraints that can be used, and/or 2) guarantees with respect to sampling accuracy. We therefore present Flexics, the first flexible pattern sampler that supports a broad class of quality measures and constraints, while providing strong guarantees regarding sampling accuracy. To achieve this, we leverage the perspective on pattern mining as a constraint satisfaction problem and build upon the latest advances in sampling solutions in SAT as well as existing pattern mining algorithms. Furthermore, the proposed algorithm is applicable to a variety of pattern languages, which allows us to introduce and tackle the novel task of sampling sets of patterns. We introduce and empirically evaluate two variants of Flexics: 1) a generic variant that addresses the well-known itemset sampling task and the novel pattern set sampling task as well as a wide range of expressive constraints within these tasks, and 2) a specialized variant that exploits existing frequent itemset techniques to achieve substantial speed-ups. Experiments show that Flexics is both accurate and efficient, making it a useful tool for pattern-based data exploration.Comment: Accepted for publication in Data Mining & Knowledge Discovery journal (ECML/PKDD 2017 journal track