Apriomining can handle convertible constraints but depends on the type ofstrains. When comes to constraints such as monotone, andanti-monotone are not accommodated into mining algorithms (Berry &Linoff, 2004). For instance when we use level framework does not needdirect pruning hence, constraining can be made and, therefore, can beconverted to the system.
Fromthe definition of the term colossal, it means a long sequence made ofa small number of an item. Therefore, colossal patterns contain moreof the core pattern. The relationship between the colossal patternand core pattern depends on the robustness. The robustness results tocore descendants which mean that when a small item or patternremoved, the resulting pattern will still have similar support set.The characteristic is similar to both colossal and core patterns(Berry & Linoff, 2004).
Boostingis a learning machine which ensemble meta-algorithm and reduces biasprimarily and also variance. Its main function is to help inimproving the learners in that it sets weaker learners create asingle strong learner. Boosting is helpful in improving accuracy bycombining decision tree. It has also used ADTree which produceshighly accurate classifiers while generating trees in small size.
Theensemble methods have been on the prime line of improving theclassification accuracy. The ensemble uses many models in improvingaccuracy. Also, it combines series of K learned models, for example,Model1, 2……….Model K with the aim of improving the accuracy.Popular methods are bagging which is used to average the predictionover a collection of classifiers also ensemble usually to combine aset of heterogeneous classifiers (Berry & Linoff, 2004).
Classificationis the association between the instances features and the class theybelong to that classification algorithms are supposed to learn. Theclassification has also belonged to supervised. For example theinsurance company trying to assign customers into high-risk andlow-risk categories (Berry & Linoff, 2004)
Clusteringon another hand based on grouping items based on the similarities ofdata instances to each other. Example online movie company wasrecommending buying certain movies since other customer made similarmovie choices.
Thetop-k ranking done by use of a top query those combines differentrankings. It outlines K objects with the highest score that dependson the aggregate function. The domains use top-k join operates. Thespace analysis also indicates the memory that requires the top-kalgorithms that perform sorted accesses. Milt way top-k join operatoralso adds upper advantage over evaluation tress of binary top-k joinoperators.
Berry,M., & Linoff, G. (2004). Datamining techniques.Indianapolis: Wiley.