5 research outputs found
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Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks
In order to gain knowledge from large databases, scalable data mining technologies are needed. Data are captured on a large scale and thus databases are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classiļ¬cation rule induction, parallelisation of classiļ¬cation rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classiļ¬cation rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classiļ¬cation rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach.are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classiļ¬cation rule induction, parallelisation of classiļ¬cation rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classiļ¬cation rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classiļ¬cation rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach
PERANGKAT LUNAK ANALISA KEMAMPUAN SISWA MENGGUNAKAN ALGORITMA PRISM
ABSTRAKĀ Perkembangan teknologi selalu mempunyai peran yang sangat penting dalam memberikan arahan pada dunia pendidikan. Salah satu arahan yang menarik adalah berkenaan dengan peningkatan mutu pendidikan. Upaya peningkatan mutu pendidikan dilakukan karena rendahnya prestasi belajar. Dalam rangka meningkatkan prestasi belajar pada siswa, guru harus bisa mengetahui kemampuan siswanya dalam menggunakan model pembelajaran serta ketrampilan dalam menyampaikan materi pelajaran. Hal ini sangat berpengaruh terhadap keberhasilan belajar siswa. Salah satu model pembelajaran yang dapat digunakan adalah model E-Learning. E-Learning merupakan suatu jenis belajar mengajar yang memungkinkan dalam menyampaikan pelajaran pada siswa dengan menggunakan media internet, intranet atau media jaringan komputer lainnya. Sehingga akan sedikit membantu siswa dalam mengerjakan soal. Sedangkan algoritma yang akan diterap pada aplikasi adalah Algoritma Prism. Output dari algoritma Prism adalah sejumlah classification rules. Algoritma ini dirancang untukĀ mengetahui materi yang dikuasai atau tidak dikuasai oleh siswa dari hasil Rule. Berdasarkan uraian yang telah dikemukakan diatas, penulis tertarik untuk mengadakan penelitian di Lembaga Kursus Pendidikan ( LKP ) Mentari Bangkit Pamekasan yang bertujuan untuk mengukur kemampuan siswa dalam menjawab soal sesuai dengan materi. Materi ini dari mata pelajaran Matematika tingkat SD kelas IV, V, dan VI dengan jumlah materi sebanyak 13 materi dan soal terdiri dari 130 soal dengan tipe soal pilihan ganda.Ā Kata Kunci : E-Learning, Classification Rules, Algoritma Prism
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J-measure based hybrid pruning for complexity reduction in classification rules
Prism is a modular classification rule generation method based on the āseparate and conquerā approach that is alternative to the rule induction approach using decision trees also known as ādivide and conquerā. Prism often achieves a similar level of classification accuracy compared with decision trees, but tends to produce a more compact noise tolerant set of classification rules. As with other classification rule generation methods, a principle problem arising with Prism is that of overfitting due to over-specialised rules. In addition, over-specialised rules increase the associated computational complexity. These problems can be solved by pruning methods. For the Prism method, two pruning algorithms have been introduced recently for reducing overfitting of classification rules - J-pruning and Jmax-pruning. Both algorithms are based on the J-measure, an information theoretic means for quantifying the theoretical information content of a rule. Jmax-pruning attempts to exploit the J-measure to its full potential because J-pruning does not actually achieve this and may even lead to underfitting. A series of experiments have proved that Jmax-pruning may outperform J-pruning in reducing overfitting. However, Jmax-pruning is computationally relatively expensive and may also lead to underfitting. This paper reviews the Prism method and the two existing pruning algorithms above. It also proposes a novel pruning algorithm called Jmid-pruning. The latter is based on the J-measure and it reduces overfitting to a similar level as the other two algorithms but is better in avoiding underfitting and unnecessary computational effort. The authors conduct an experimental study on the performance of the Jmid-pruning algorithm in terms of classification accuracy and computational efficiency. The algorithm is also evaluated comparatively with the J-pruning and Jmax-pruning algorithms