Practical Aspects of Data Mining Using LISp-Miner

The paper describes some practical aspects of using LISp-Miner for data mining. LISp-Miner is a software tool that is under development at the University of Economics, Prague. We will review the different types of knowledge patterns discovered by the system, and discuss their applicability for various data mining tasks. We also compare LISp-Miner 18.16 with Weka 3.6.9 and Rapid Miner 5.3

An Effective Prediction Factors for Coronary Heart Disease using Data Mining based Classification Technique

Identification of diseases are very challenging task in field of medical science. Heart disease is very critical issues facing by the people. In our proposed work we have used data mining based classification techniques for analysis and classification of different level of heart disease namely Cleveland, Switzerland, Hungarian and Long Beach. We have used WEKA and Rapid miner data mining tools for analysis of heart disease data set and compared the performance of different classification techniques with four heart disease data set using WEKA and Rapid Miner data mining tool. The proposed SVM gives better accuracy as 66.67% with Hungarian data set in case of WEKA data mining tool while Decision Stump gives better accuracy as 63.94% with same Hungarian data set in case of Rapid miner data mining tool. The Hungarian data set gives better performance with our proposed data mining tools and classification techniques which can help the people to predict effective factors about Coronary Heart Disease

Implementasi Algoritma K-means Clustering untuk Menentukan Arketipe Pembelian Suku Cadang dan Asesoris Komputer(studi Kasus di Toko Laksamana Komputer Dumai)

Penelitian ini dilakukan untuk mempelajari Data Mining K-Means Clustering dalam pencarian knowledge (pengetahuan). Tujuan dari penelitian ini kemungkinan dapat membantu pihak Laksamana Komputer Dumai untuk menentukan assesoris dan spare part computer mana yang pembeli paling banyak dan diletakkan pada posisi paling depan berdasarkan permintaan konsumen di Laksamana Komputer Dumai.Untuk itu dalam metode K-Means Clustering dimungkinkan adanya solusi dan analisa terhadap pengolahan data dan parameter-parameter yang menjadi acuan untuk mengambil keputusan. Di dalam metode ini terdapat langkah-langkah penyelesaian masalah. Adapun tools bantu untuk mengimplementasikan metode tersebut adalah Rapid miner 7.3. Rapid miner 7.3 akan mengolah data secara tersusun atas operator-operator yang nestable yang langsung didapatkan hasil secara akurat selanjutnya pada tahapan terakhir akan didapatkan knowledge baru

Penerapan Data Mining untuk Menentukan Jumlah Pencari Kerja Terdaftar Berdasarkan Umur dan Pendidikan Menggunakan K-Means Clustering (Studi Kasus di Dinas Tenaga Kerja dan Transmigrasi Provinsi Bengkulu)

Jumlah pencari kerja terdaftar terdiri dari pendaftar berdasarkan umur dan pendidikan. Masing masing memiliki jumlah pendaftar yang berbeda, dengan pendaftar berdasarkan umur memliki beberapa kategori dan berdasarkan pendidikan memiliki variasi pendidikan yang dimiliki pencari kerja terdaftar. Penelitian ini menerapkan Data Mining dengan menggunakan metode Clustering untuk menentukan jumlah pencari kerja berdasarkan tingkat jumlah pendaftar di Dinas Tenaga Kerja dan Transmigrasi Provinsi Bengkulu. Algoritma yang digunakan yaitu K-Means Clustering, di mana data dikelompokkan berdasarkan karakteristik yang sama akan dimasukkan ke dalam kelompok yang sama dan set data yang dimasukkan ke dalam kelompok tidak tumpang tindih.Pengujian dilakukan dengan aplikasi Rapid Miner 5.3. Rapid Miner merupakan software Data Mining yang dapat digunakan untuk mengakses beberapa metode yang ada dalam Data Mining, sehingga dapat menghasilkan cluster-cluster dalam pengelompokan data jumlah pencari kerja yang terdaftar

Data mining methods for the prediction of different forms of asthma

The article examines the diagnosis of bronchial asthma, cites the classification of the disease, proves the relevance of this research, and represents the result of primary data analysis by using a powerful tool for data analysis - Rapid Miner

Analysis of Student’s Data Using Rapid Miner

Data mining offers a new advance to data analysis using techniques based on machine learning, together with the conventional methods collectively known as educational data mining (EDM). Educational Data Mining has turned up as an interesting and useful research area for finding methods to improve quality of education and to identify various patterns in educational settings. It is useful in extracting information of students, teachers, courses, administrators from educational institutes such as schools/colleges/universities and helps to suggest interesting learning experiences to various stakeholders. This paper focuses on the applications of data mining in the field of education and implementation of three widely used data mining techniques using Rapid Miner on the data collected through a survey.

Data mining methods for the prediction of different forms of asthma

The article examines the diagnosis of bronchial asthma, cites the classification of the disease, proves the relevance of this research, and represents the result of primary data analysis by using a powerful tool for data analysis - Rapid Miner

Analysis of Student's Data using Rapid Miner

Data mining offers a new advance to data analysis using techniques based on machine learning, together with the conventional methods collectively known as educational data mining (EDM). Educational Data Mining has turned up as an interesting and useful research area for finding methods to improve quality of education and to identify various patterns in educational settings. It is useful in extracting information of students, teachers, courses, administrators from educational institutes such as schools/ colleges/universities and helps to suggest interesting learning experiences to various stakeholders. This paper focuses on the applications of data mining in the field of education and implementation of three widely used data mining techniques using Rapid Miner on the data collected through a survey

Image data segmentation

Na začátku diplomové práce je čtenář seznámen s procesem zpracování obrazu a v navazující části jsou popsáný a vysvětleny dnes nejpoužívanější algoritmy pro segmentaci obrazu. Na základě watershed transform je vytvořen segmentační operátor pro volně šiřitelný program Rapid Miner a v dokumentu je popsáno, jak proces vývoje probíhal. V poslední části práce jsou prezentovány segmentované obrazy a popsána úskalí takto implementované watershed transform metody.A reader is acquaint with image segmentation process at the beginning of the master thesis and the most popular algorithms for image segmentation are explained and covered in the following part. Segmentation operator for Rapid Miner freeware program was created on basics of watershed transform; and in the paper was described process of development. In last section of the work segmented images are presented; and diculties of this watershed transform implementation are described.

Sentiment Analysis using Rapid Miner for Polarity Dataset

Usage of social media like whatsapp, facebook, twitter, blogs etc is increasing day by day which makes every people to feel free to comment and share their views, opinions and suggestions which can be either positive, negative or neutral comments on various topics like politics, business, advertisement, entertainment etc. This may contain likes, dislikes, good, bad or Emotions etc which are nothing but some type of sentiments. Judging these sentiments helps to find out whether the given sentiment is positive, negative or neutral by using sentiment analysis. In this paper we are discussing about the concept of polarity in sentiment analysis by using polarity movie review dataset from Bo Pang and Lillian Lee. DOI: 10.17762/ijritcc2321-8169.15082