Mining frequent itemsets a perspective from operations research

Many papers on frequent itemsets have been published. Besides somecontests in this field were held. In the majority of the papers the focus ison speed. Ad hoc algorithms and datastructures were introduced. Inthis paper we put most of the algorithms in one framework, usingclassical Operations Research paradigms such as backtracking, depth-first andbreadth-first search, and branch-and-bound. Moreover we presentexperimental results where the different algorithms are implementedunder similar designs.data mining;operation research;Frequent itemsets

Proses Inovasi pada Klaster Kampoeng Batik Laweyan Kota Surakarta

Pelaku USAha di Klaster Kampoeng Batik Laweyan menerapkan inovasi sebagai solusi dari permasalahan yang ada. Inovasi-inovasi ini diterapkan pada rantai nilai produksi (pada input, proses, output dan pemasaran). Sumber inovasi berasal dari pelaku USAha dalam klaster, permintaan pasar atau konsumen, dan pihak akedemisi. Pelopor utama pada klaster ini adalah Alpha Fabela, Bambang Slamento dan Saud Efendi. Motivasi utama mereka melakukan inovasi untuk meneruskan industri batik yang sudah menjadi warisan budaya turun temurun sedangkan motivasi utama pengikut melakukan inovasi adalah keuntungan ekonomi. Proses adopsi inovasi-inovasi ini mengalami beberapa tahapan, yaitu: kesadaran, ketertarikan, percobaan, evaluasi dan pengangkatan. Sedangkan difusi inovasi terjadi melalui saluran komunikasi FPKBL dan interaksi sosial pelaku USAha. Inovasi-inovasi ini berhasil meningkatkan daya saing produk batik Laweyan, terlihat dari wilayah pemasarannya yang tidak hanya di dalam negeri tetapi sampai ke luar negeri

SemGrAM - Integrating semantic graphs into association rule mining

To date, most association rule mining algorithms have assumed that the domains of items are either discrete or, in a limited number of cases, hierarchical, categorical or linear. This constrains the search for interesting rules to those that satisfy the specified quality metrics as independent values or as higher level concepts of those values. However, in many cases the determination of a single hierarchy is not practicable and, for many datasets, an item’s value may be taken from a domain that is more conveniently structured as a graph with weights indicating semantic (or conceptual) distance. Research in the development of algorithms that generate disjunctive association rules has allowed the production of rules such as Radios V TVs -> Cables. In many cases there is little semantic relationship between the disjunctive terms and arguably less readable rules such as Radios V Tuesday -> Cables can result. This paper describes two association rule mining algorithms, SemGrAMG and SemGrAMP, that accommodate conceptual distance information contained in a semantic graph. The SemGrAM algorithms permit the discovery of rules that include an association between sets of cognate groups of item values. The paper discusses the algorithms, the design decisions made during their development and some experimental results.Sydney, NS

Mining frequent itemsets a perspective from operations research

Many papers on frequent itemsets have been published. Besides some contests in this field were held. In the majority of the papers the focus is on speed. Ad hoc algorithms and datastructures were introduced. In this paper we put most of the algorithms in one framework, using classical Operations Research paradigms such as backtracking, depth-first and breadth-first search, and branch-and-bound. Moreover we present experimental results where the different algorithms are implemented under similar designs

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Algorithmic tools for data-oriented law enforcement

The increase in capabilities of information technology of the last decade has led to a large increase in the creation of raw data. Data mining, a form of computer guided, statistical data analysis, attempts to draw knowledge from these sources that is usable, human understandable and was previously unknown. One of the potential application domains is that of law enforcement. This thesis describes a number of efforts in this direction and reports on the results reached on the application of its resulting algorithms on actual police data. The usage of specifically tailored data mining algorithms is shown to have a great potential in this area, which forebodes a future where algorithmic assistance in "combating" crime will be a valuable asset.NWOUBL - phd migration 201

Mining Clusters with Association Rules

Contains fulltext : 84506.pdf (author's version ) (Open Access)Third International Symposium on Advances in Intelligent Data Analysi