i-Eclat: performance enhancement of eclat via incremental approach in frequent itemset mining

One example of the state-of-the-art vertical rule mining technique is called equivalence class transformation (Eclat) algorithm. Neither horizontal nor vertical data format, both are still suffering from the huge memory consumption. In response to the promising results of mining in a higher volume of data from a vertical format, and taking consideration of dynamic transaction of data in a database, the research proposes a performance enhancement of Eclat algorithm that relies on incremental approach called an Incremental-Eclat (i-Eclat) algorithm. Motivated from the fast intersection in Eclat, this algorithm of performance enhancement adopts via my structured query language (MySQL) database management system (DBMS) as its platform. It serves as the association rule mining database engine in testing benchmark frequent itemset mining (FIMI) datasets from online repository. The MySQL DBMS is chosen in order to reduce the preprocessing stages of datasets. The experimental results indicate that the proposed algorithm outperforms the traditional Eclat with 17% both in chess and T10I4D100K, 69% in mushroom, 5% and 8% in pumsb_star and retail datasets. Thus, among five (5) dense and sparse datasets, the average performance of i-Eclat is concluded to be 23% better than Eclat

Postdiffset Algorithm in Rare Pattern: An Implementation via Benchmark Case Study

Frequent and infrequent itemset mining are trending in data mining techniques. The pattern of Association Rule (AR) generated will help decision maker or business policy maker to project for the next intended items across a wide variety of applications. While frequent itemsets are dealing with items that are most purchased or used, infrequent items are those items that are infrequently occur or also called rare items. The AR mining still remains as one of the most prominent areas in data mining that aims to extract interesting correlations, patterns, association or casual structures among set of items in the transaction databases or other data repositories. The design of database structure in association rules mining algorithms are based upon horizontal or vertical data formats. These two data formats have been widely discussed by showing few examples of algorithm of each data formats. The efforts on horizontal format suffers in huge candidate generation and multiple database scans which resulting in higher memory consumptions. To overcome the issue, the solutions on vertical approaches are proposed. One of the established algorithms in vertical data format is Eclat.ECLAT or Equivalence Class Transformation algorithm is one example solution that lies in vertical database format. Because of its, fast intersection‟, in this paper, we analyze the fundamental Eclat and Eclatvariants such asdiffsetand sortdiffset. In response to vertical data format and as a continuity to Eclat extension, we propose a postdiffset algorithm as a new member in Eclat variants that use tidset format in the first looping and diffset in the later looping. In this paper, we present the performance of Postdiffset algorithm prior to implementation in mining of infrequent or rare itemset.Postdiffset algorithm outperforms 23% and 84% to diffset and sortdiffset in mushroom and 94% and 99% to diffset and sortdiffset in retail dataset

Analysis study on R-Eclat algorithm in infrequent itemsets mining

There are rising interests in developing techniques for data mining. One of the important subfield in data mining is itemset mining, which consists of discovering appealing and useful patterns in transaction databases. In a big data environment, the problem of mining infrequent itemsets becomes more complicated when dealing with a huge dataset. Infrequent itemsets mining may provide valuable information in the knowledge mining process. The current basic algorithms that widely implemented in infrequent itemset mining are derived from Apriori and FP-Growth. The use of Eclat-based in infrequent itemset mining has not yet been extensively exploited. This paper addresses the discovery of infrequent itemsets mining from the transactional database based on Eclat algorithm. To address this issue, the minimum support measure is defined as a weighted frequency of occurrence of an itemsets in the analysed data. Preliminary experimental results illustrate that Eclat-based algorithm is more efficient in mining dense data as compared to sparse data

Fingerprint image segmentation using hierarchical technique

Fingerprints are the ridge and furrow patterns on the tip or the finger and its verification is an important biometric technique (or personal identification). The quality or the fingerprint image is the most significant (actor in a reliable matching process. Thus, any pre-processing algorithm should aim to enhance the quality of the existing features without creating false features [I). This scenario brings the idea or the research. The research rocus on the segmentation approach. The purpose of the study is to identify a segmentation approach of fingerprint image by considering several approaches namely Hierarchical technique and Region growing by pixel aggregation technique. The process is vital when to apply next pre-processing stages i.e. thinning, identifying between true and false minutiae and minutiae extraction process. The research is supported by 50 samples or data that is collected through an available fingerprint device

Fingerprint image segmentation using hierarchical technique

Cap jari merupakan corak batas dan galur yang terdapat pada jari manusia dimana pengesahan pada setiap cap jari merupakan teknik biometrik yang penting untuk pengenalan seseorang individu. Tahap kualiti pada imej cap jari merupakan faktor yang signifikan untuk menjalani proses pemadanan yang boieh dipercayai. Oieh sebab itu, setiap algoritma yang melibatkan peringkat pra-pemprosesan perlu menekankan kepada peningkatan tahap kualiti ciri-ciri yang ada pada imej cap jari tanpa mewujudkan ciri-ciri yang salah. Senario sebegini memberi idea kepada kajian untuk meneroka dan menumpukan kepada teknik segmentasi di dalam peringkat prapemprosesan imej. Tujuan kajian dibuat adalah untuk mengenatpasti pendekatan yang ada dalam proses segmentasi. Antara pendekatan yang diambilkira ialah Teknik Hirarki dan juga Teknik Region by pixel aggregation. Proses segmentasi penting untuk pengendalian proses yang seterusnya seperti penipisan, penentuan antara minutiae yang benar atau yang saiah dan juga proses pengekstrakan Kajian ini dibantu oleh 50 sampel data yang dikumpul melalui peranti imej cap jari yang disediakan. Kajian ini dibangunkan dengan menggunakan perisian Delphi 3.0

Integration model for multiple types of spatial and non spatial databases

Integration process of a various information in various database types requires a thorough understanding to carry out data extraction process in terms of its scheme and the structure. Due to this, a new model should be developed to resolve the integration process of this heterogeneous information in various database types and in various scattered and distributed locations. SIDIM is a model which covered processes such as pre-integration, scheme comparison, algorithm and intermediary software (middleware) development process and as well as post-integration. Emphasis are administered in algorithm development by using hybrid approach based on CLARANS approach's combination, abstract visualization and Catch Per Unit Effort (CPUE) to enable to achieve the required processed data or information in a quick, trusted and reliable manner. SIDIM will become a new engine to process information in various database types without changing any of the existing (legacy) organization system. To verify this model credibility, the case study related to fishing industry in Malaysia and artificial reef project are being made as a foundation for SIDIM efficiency testing. © 201