Implementing Sequential Prefixspan Algorithm by Using Static Load Balancing

Repeated series mining is well known and well studied trouble in information mining. The productivity of the formula is used in several alternative regions like chemistry, bioinformatics, and market basket analysis. A completely unique parallel algorithmic rule for mining of frequent sequences supported a static load-balancing is planned. The static load balancing is done by measure the machine time using a probabilistic algorithm. For cheap size of instance, the algorithms deliver the good speedups. The conferred approach is extremely universal: it is often used for static load-balancing of alternative pattern mining algorithms like item set/tree/graph mining algorithms

Tikimybinis dažnų posekių paieškos algoritmas

Dažnų posekių paieška didelėse duomenų bazėse yra svarbi biologinių, klimato, fi nansinių ir daugelio kitų duomenų bazių analizei. Tikslieji algoritmai, skirti dažnų posekių paieškai, daug kartų perrenka visą duomenų bazę. Jeigu duomenų bazė didelė, tai dažnų posekių paieška yra lėta arba reikalingi superkompiuteriai. Straipsnyje pasiūlytas naujas tikimybinis dažnų posekių paieškos algoritmas, kuris analizuoja tam tikru būdu sudarytą pradinės duomenų bazės atsitiktinę imtį. Remiantis šia analizedaromos statistinės išvados apie dažnus posekius pradinėje duomenų bazėje. Šis algoritmas nėra tikslus, tačiau veikia daug greičiau negu tikslieji algoritmai ir tinka žvalgomajai statistinei analizei. Tikimybinio algoritmo klaidų tikimybės įvertinamos statistiniais metodais. Tikimybinis algoritmas gali būti derinamas su tiksliaisiais dažnų posekių paieškos algoritmais. Jį galima taikyti ir bendrajam struktūrų paieškos uždaviniui.Probabilistic Algorithm for Mining Frequent SequencesJulija Pragarauskaitė, Gintautas Dzemyda SummaryFrequent sequence mining in large volume databases is important in many areas, e.g., biological, climate, fi nancial databases. Exact frequent sequence mining algorithms usually read the whole database many times, and if the database is large enough, then frequent sequence mining is very long or requires supercomputers. A new probabilistic algorithm for mining frequent sequences is proposed. It analyzes a random sample of the initial database. The algorithm makes decisions about the initial database according to the random sample analysis results and performs much faster than the exact mining algorithms. The probability of errors made by the probabilistic algorithm is estimated using statistical methods. The algorithm can be used together with the exact frequent sequence mining algorithms

Problem-Solving Knowledge Mining from Users’\ud Actions in an Intelligent Tutoring System

In an intelligent tutoring system (ITS), the domain expert should provide\ud relevant domain knowledge to the tutor so that it will be able to guide the\ud learner during problem solving. However, in several domains, this knowledge is\ud not predetermined and should be captured or learned from expert users as well as\ud intermediate and novice users. Our hypothesis is that, knowledge discovery (KD)\ud techniques can help to build this domain intelligence in ITS. This paper proposes\ud a framework to capture problem-solving knowledge using a promising approach\ud of data and knowledge discovery based on a combination of sequential pattern\ud mining and association rules discovery techniques. The framework has been implemented\ud and is used to discover new meta knowledge and rules in a given domain\ud which then extend domain knowledge and serve as problem space allowing\ud the intelligent tutoring system to guide learners in problem-solving situations.\ud Preliminary experiments have been conducted using the framework as an alternative\ud to a path-planning problem solver in CanadarmTutor