An Efficient Itemset Representation for Mining Frequent Patterns in Transactional Databases

In this paper we propose very efficient itemset representation for frequent itemset mining from transactional databases. The combinatorial number system is used to uniquely represent frequent k-itemset with just one integer value, for any k ≥ 2. Experiments show that memory requirements can be reduced up to 300 %, especially for very low minimal support thresholds. Further, we exploit combinatorial number schema for representing candidate itemsets during iterative join-based approach. The novel algorithm maintains one-dimensional array rank, starting from k = 2nd iteration. At the index r of the array, the proposed algorithm stores unique integer representation of the r-th candidate in lexicographic order. The rank array provides joining of two candidate k-itemsets to be O(1) instead of O(k) operation. Additionally, the rank array provides faster determination which candidates are contained in the given transaction during the support count and test phase. Finally, we believe that itemset ranking by combinatorial number system can be effectively integrated into pattern-growth algorithms, that are state-of-the-art in frequent itemset mining, and additionally improve their performances

Data Mining Approach in Climate Classification and Climate Network Construction – Case Study Montenegro

In this study, we present results of applying data mining techniques on meteorological dataset obtained from the Institute of Hydrometeorology and Seismology of Montenegro. The dataset covers the measurements taken from all 11 main meteorological stations in Montenegro for the period 2010-2015. We build new climate classification system based on decision tree. The system is simpler (i.e. uses fewer attributes) and more accurate than the well-known Köppen climate classification system. In addition, we propose a novel procedure for climate network construction. Finally, we identify the regions within the same climate type in Montenegro’s climate network with the Girvan-Newman algorithm for community detection and achieve better results with respect to classical K-means and hierarchical clustering algorithms

Cognitive Approach in Document Indexing

Even though the digital processing of documents is increasingly widespread in industry, printed documents are still largely in use. Datum Solutions Cognitive Capture implements the automatic processing of administrative documents that need to be treated in a close to real time manner. The software can handle complex documents, in which the contents of different regions and fields can be highly heterogeneous with respect to layout, printing quality and the utilization of fonts and typing standards

Comparative Analysis of Classic Clustering Algorithms and Girvan-Newman Algorithm for Finding Communities in Social Networks

Nowadays finding patterns in large social network datasets is a growing challenge and an important subject of interest. One of current problems in this field is identifying clusters within social networks with large number of nodes. Social network clusters are not necessarily disjoint sets; rather they may overlap and have common nodes, in which case it is more appropriate to designate them as communities. Although many clustering algorithms handle small datasets well, they are usually extremely inefficient on large datasets. This paper shows comparative analysis of frequently used classic graph clustering algorithms and well-known Girvan-Newman algorithm that is used for identification of communities in graphs, which is especially optimized for large datasets. The goal of the paper is to show which of the algorithms give best performances on given dataset. The paper presents real problem of data clustering, algorithms that can be used for its solution, methodology of analysis, results that were achieved and conclusions that were derived. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Comparative Analysis of Classic Clustering Algorithms and Girvan-Newman Algorithm for Finding Communities in Social Networks

Nowadays finding patterns in large social network datasets is a growing challenge and an important subject of interest. One of current problems in this field is identifying clusters within social networks with large number of nodes. Social network clusters are not necessarily disjoint sets; rather they may overlap and have common nodes, in which case it is more appropriate to designate them as communities. Although many clustering algorithms handle small datasets well, they are usually extremely inefficient on large datasets. This paper shows comparative analysis of frequently used classic graph clustering algorithms and well-known Girvan-Newman algorithm that is used for identification of communities in graphs, which is especially optimized for large datasets. The goal of the paper is to show which of the algorithms give best performances on given dataset. The paper presents real problem of data clustering, algorithms that can be used for its solution, methodology of analysis, results that were achieved and conclusions that were derived. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Effect of leaf treatment with cobalt chloride on adventitious rooting of cottonwood (populus deltoides bartr. Ex marsh) cuttings

The influence of CoCl2 on cutting rooting of five eastern cottonwood genotypes was examined. Rooted cuttings were treated with 100 mu M CoCl2 four weeks after the planting and morphological rooting characteristics were measured eight weeks after the experiment establishment. According to LSD-test treatment with cobalt chloride had positive effect on almost all examined characters, but only in number of roots on the basal 5 cm of cutting, number of roots from the 5th to 10th cm of cutting, number of roots on basal 10 cm of cutting, and total number of roots that effect was statistically significant. The obtained results indicate that for some difficult-to-root genotypes the treatment with cobalt could have a positive effect on rooting