scholarly journals Recommendation System for Movie Cast and Crew using Datamining Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 1495-1497
Keyword(s):  
Data Mining ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Apriori Algorithm ◽  
Data Mining Algorithms ◽  
The Right ◽  
Mining Algorithms

In this article, the data mining algorithms like apriori algorithm is used to suggest the best cast and crew to make a particular genre of movies so that the movie is successful. The data of cast and crew is extracted for which the users have given high average ratings and apply apriori algorithm to give recommendation. There are recommendation systems which gives the recommendation to the users so that the users can watch movies in which they are interested in. But there are no recommendation systems which gives the right information which is helpful for movie making. It is important to make good movies so that the viewers and entertained by it due to which the profit of the producers increases. This research work can be used by the movie makers to select the best cast and crew for the particular genre of movie.

scholarly journals An Extended Recommendation System using Data Mining Implemented for Smart Phones

2013 ◽  
Vol 11 (3) ◽  
pp. 2360-2372 ◽  
Author(s):  
Robert Gyorodi ◽  
Cornelia Gyorodi ◽  
Mihai Dersidan
Keyword(s):  
Data Mining ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Smart Phones ◽  
Rating System ◽  
Data Mining Algorithms ◽  
Rating Method ◽  
Using Data ◽  
Mining Algorithms

In this paper we introduce a recommendation system that attempts to solve some of the issues of classical recommendation systems: the need for huge amounts of data to be able to extract meaningful patterns, or for directly visible connections between the users. We propose a rating method that is based on a hierarchical tag system used to describe the subjects of recommendation, and two data mining algorithms that run on the data gathered through this rating system.

scholarly journals Application of Data Mining to E-Commerce Recommendation Systems

2018 ◽  
Vol 7 (2.32) ◽  
pp. 420
Author(s):  
Dr P.V.R.D. Prasad Rao ◽  
S Varakumari ◽  
Vineetha B ◽  
V Satish
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Information Storage ◽  
Apriori Algorithm ◽  
Rising Power

The rising power of technology has intensely improved the information storage, collection, and manipulation ability. As the information is growing very rapid along with its complexness, data analysis has become more important. The aim of this paper is to recommend products to the user which are more likely to be purchased. This paper, first describes about different techniques for recommendation and the research regarding recommendation system, then suggests a better approach for a good recommendation system and explains the results of that approach. Here, a combination of k-means clustering algorithm and apriori algorithm on transactional dataset so that a better recommendation list can be obtained. 

Penggunaan Association Rule Data Mining Untuk Menentukan Pola Lama Studi Mahasiswa F-MIPA UNSRAT

d'CARTESIAN ◽  
2014 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
M. Zainal Mahmudin ◽  
Altien Rindengan ◽  
Winsy Weku
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Large Data ◽  
Apriori Algorithm ◽  
Data Mining Algorithms ◽  
Adequate Information ◽  
Processing Data ◽  
Rule Method ◽  
Mining Algorithms ◽  
Confidence Value

Abstract The requirement of highest information sometimes is not balance with the provision of adequate information, so that the information must be re-excavated in large data. By using the technique of association rule we can obtain information from large data such as the college data. The purposes of this research is to determine the patterns of study from student in F-MIPA UNSRAT by using association rule method of data mining algorithms and to compare in the apriori method and a hash-based algorithms. The major’s student data of F-MIPA UNSRAT as a data were processed by association rule method of data mining with the apriori algorithm and a hash-based algorithm by using support and confidance at least 1 %. The results of processing data with apriori algorithms was same with the processing results of hash-based algorithms is as much as 49 combinations of 2-itemset. The pattern that formed between 7,5% of graduates from mathematics major that studied for more 5 years with confidence value is 38,5%. Keywords: Apriori algorithm, hash-based algorithm, association rule, data mining. Abstrak Kebutuhan informasi yang sangat tinggi terkadang tidak diimbangi dengan pemberian informasi yang memadai, sehingga informasi tersebut harus kembali digali dalam data yang besar. Dengan menggunakan teknik association rule kita dapat memperoleh informasi dari data yang besar seperti data yang ada di perguruan tinggi. Tujuan penelitian ini adalah menentukan pola lama studi mahasiswa F-MIPA UNSRAT dengan menggunakan metode association rule data mining serta membandingkan algoritma apriori dan algoritma hash-based. Data yang digunakan adalah data induk mahasiswa F-MIPA UNSRAT yang  diolah menggunakan teknik association rule data mining dengan algoritma apriori dan algoritma hash-based dengan minimum support 1% dan minimum confidance 1%. Hasil pengolahan data dengan algoritma apriori sama dengan hasil pengolahan data dengan algoritma hash-based yaitu sebanyak 49 kombinasi 2-itemset. Pola yang terbentuk antara lain 7,5% lulusan yang berasal dari jurusan matematika menempuh studi selama lebih dari     5 tahun dengan nilai confidence 38,5%. Kata kunci : Association rule data mining, algoritma apriori, algoritma hash-based

Environmental Examination of Data Mining Algorithms Based on Cloud-Computing

2013 ◽  
Vol 380-384 ◽  
pp. 2911-2914
Author(s):  
Yi Zhuo Guo ◽  
Tao Dai
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Cluster Computing ◽  
Programming Model ◽  
Test System ◽  
Parallel Execution ◽  
Apriori Algorithm ◽  
Data Mining Algorithms ◽  
Parallel Programming Model ◽  
Mining Algorithms

This article on cloud computing and data mining to a more comprehensive study to introduce the concept of cloud computing and data mining, pointed out that the traditional data mining techniques in the case of network test system of massive data mining, processing speed is slow, the load is not balancing and node efficiency is not high enough, Apriori algorithm based on the Map/Reduce parallel programming model, the distributed nature of cloud computing environments, make full use of cluster computing resources to support the parallel execution of algorithms by examples of cloud computing after Apriori algorithm in cloud computing environment to get higher efficiency of frequent itemsets mining algorithm performance than traditional data mining.

scholarly journals Algoritma Apriori Untuk Penentuan Assosiasi Penjualan Barang

2021 ◽  
Vol 9 (1) ◽  
pp. 7
Author(s):  
Calvin Ivan Wiryawan ◽  
Yustina Retno Wahyu Utami ◽  
Didik Nugroho
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Basic Needs ◽  
Marketing Strategies ◽  
Apriori Algorithm ◽  
Growing Up ◽  
Minimum Support ◽  
Data Mining Algorithms ◽  
Mining Algorithms

The increasing of selling basic needs make the company has to provide a lot of goods. The data will be growing up with increasing the transaction at Sari Bumi store. All this time, the selling basic needs at Sari Bumi Store unstructured well so that needed an application with produce important information that can decide marketing strategies. In this research, Apriori algorithm is used to determine association rules. This method was chosen because it is one of the classic data mining algorithms to look for patterns of relationships between one or more items in one dataset. A priori algorithms can help companies in developing marketing strategies. The result of this research is combination between 4 item set with a minimum support of 30% and minimum confidence of 60%.Keywords: sale, staple, apriori algorithm

scholarly journals Analisis Algoritma Apriori untuk Mendukung Strategi Promosi Perguruan Tinggi

2019 ◽  
Vol 1 (1) ◽  
pp. 49
Author(s):  
Haryo Kusumo ◽  
Eko Sediyono ◽  
Marwata Marwata
Keyword(s):  
Data Mining ◽  
A Priori ◽  
Promotion Strategy ◽  
Data Mining Techniques ◽  
Data Mining Algorithms ◽  
New Students ◽  
The Right ◽  
The Cost ◽  
Mining Algorithms

<p><em>Every company and organization that wants to survive needs to determine the effectiveness of the right promotion strategy. Determination of the right promotion strategy will be able to reduce the cost of promotion and achieve the right promotional goals. One way that can be done to determine the promotion strategy is to use data mining techniques. Data mining techniques used in this case are using the Apriori algorithm. A priori algorithm is one of the classic data mining algorithms. A priori algorithms are used so that computers can learn the rules of association, look for patterns of relationships between one or more items in a dataset. This study is conducted by observing several research variables that are often considered by universities in determining their promotion goals, namely school, region, and department. The results of this study are in the form of interesting patterns resulting from data mining which is important information to support the right promotion strategy in getting new students.</em></p>

scholarly journals Comparative Study of Rainfall Prediction Modeling Techniques (A Case Study on Srinagar, J&K, India)

2018 ◽  
Vol 7 (3) ◽  
pp. 13-19
Author(s):  
Razeef Mohd ◽  
Muheet Ahmed Butt ◽  
Majid Zaman Baba
Keyword(s):  
Data Mining ◽  
Process Model ◽  
Weather Prediction ◽  
Research Work ◽  
Weather Data ◽  
Correct Prediction ◽  
Rainfall Prediction ◽  
Data Mining Algorithms ◽  
Few Data ◽  
Mining Algorithms

Prediction of rainfall is one of the most essential and demanding tasks for the weather forecasters since ages. Rainfall prediction plays an important role in the field of farming and industries. Precise rainfall prediction is vital for detecting the heavy rainfall and to provide the information of warnings regarding the natural calamities. Rainfall prediction involves recording the various parameters of weather like wind direction, wind speed, humidity, rainfall, temperature etc. From last few decades, it has been seen that data mining techniques have achieved good performance and accuracy in weather prediction than traditional statistical methods. This research work aims to compare the performance of few data mining algorithms for predicting rainfall using historical weather data of Srinagar, India, which is collected from http://www.wundergrounds.com website. From the collected weather data which comprises of 9 attributes, only 5 attributes which are most relevant to rainfall prediction are considered. Data mining process model is followed to obtain accurate and correct prediction results. In this paper, various data mining algorithms were explored which include decision tree based J48, Random forest, Naive Bayes, Bayes Net, Logistic Regression, IBk, PART and bagging. The experimental results show that J48 algorithm has good level of accuracy than other algorithms.

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