scholarly journals TV Show Popularity Analysis Using Data Mining

2021 ◽  
Vol 9 (12) ◽  
pp. 147-152
Author(s):  
Deesha Rawat
Keyword(s):  
Data Mining ◽  
Data Visualization ◽  
Analysis Data ◽  
Target Audience ◽  
Tv Shows ◽  
Using Data ◽  
Tv Show

Abstract: Evaluating and updating TV programs using data mining. Guessing the popularity of the TV show is an exciting activity with a growing interest in people in TV dramas. A simple guess of a trending TV game based on individual ratings can be made. Simple predictions of trending TV shows based on individual ratings can be made based on the target audience (Age, Year of Release, Rotten Tomatoes, etc.). Keywords: TV Show, Data Mining, Popularity Analysis, Data Visualization, IMDB Dataset

Finding Patterns in Class-Labeled Data Using Data Visualization

2011 ◽  
pp. 106-123
Author(s):  
Gregor Leban ◽  
Minca Mramor ◽  
Blaž Zupan ◽  
Janez Demšar ◽  
Ivan Bratko
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Data Analysis ◽  
Data Visualization ◽  
Gene Expression Data ◽  
Data Sets ◽  
Cancer Gene ◽  
Manual Search ◽  
Degree Of Separation ◽  
Using Data

Data visualization plays a crucial role in data mining and knowledge discovery. Its use is, however, often difficult due to the large number of possible data projections. Manual search through such sets of projections can be prohibitively timely or even impossible, especially in the data analysis problems that comprise many data features. The chapter describes a method called VizRank, which can be used to automatically identify interesting data projections for multivariate visualizations of class-labeled data. VizRank assigns a score of interestingness to each considered projection based on the degree of separation of data instances with different class label. We demonstrate the usefulness of this approach on six cancer gene expression data sets, showing that the method can reveal interesting data patterns and can further be used for data classification and outlier detection.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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