Seasonal to Inter-annual Climate Prediction Using Data Mining KNN TYechnique”,

Abstract. The impact of seasonal to inter-annual climate prediction on society, business, agriculture and almost all aspects of human life, force the scientist to give proper attention to the matter. The last few years show tremendous achievements in this field. All systems and techniques developed so far, use the Sea Surface Temperature (SST) as the main factor, among other seasonal climatic attributes. Statistical and mathematical models are then used for further climate predictions. In this paper, we develop a system that uses the historical weather data of a region (rain, wind speed, dew point, temperature, etc.), and apply the data-mining algorithm "K-Nearest Neighbor (KNN)" for classification of these historical data into a specific time span. The k nearest time spans (k nearest neighbors) are then taken to predict the weather. Our experiments show that the system generates accurate results within reasonable time for months in advance

Data Mining Techniques for Weather Prediction: A Review

Data mining is the computer assisted process of digging through and analysing enormous sets of data and then extracting the meaningful data. Data mining tools predicts behaviours and future trends, allowing businesses to make proactive decisions. It can answer questions that traditionally were very time consuming to resolve. Therefore they can be used to predict meteorological data that is weather prediction. Weather prediction is a vital application in meteorology and has been one of the most scientifically and technologically challenging problems across the world in the last century. Predicting the weather is essential to help preparing for the best and the worst of the climate. Accurate Weather Prediction has been one of the most challenging problems around the world. Many weather predictions like rainfall prediction, thunderstorm prediction, predicting cloud conditions are major challenges for atmospheric research. This paper presents the review of Data Mining Techniques for Weather Prediction and studies the benefit of using it. The paper provides a survey of available literatures of some algorithms employed by different researchers to utilize various data mining techniques, for Weather Prediction. The work that has been done by various researchers in this field has been reviewed and compared in a tabular form. For weather prediction, decision tree and k-mean clustering proves to be good with higher prediction accuracy than other techniques of data mining

Penerapan Rough Set Dan Fuzzy Rough Set Untuk Klasifikasi Data Tidak Lengkap

Data mining merupakan salah satu proses untuk menemukan pola dan pengetahuan dari database. Sebagian besar database di dunia nyata tidak dapat dihindari dari masalah ketidaklengkapan. Hal ini disebabkan antara lain oleh kesalahan prosedur manual entri data, pengukuran yang salah, dan kesalahan peralatan. Salah satu database yang tidak terlepas dari masalah ketidaklengkapan adalah dataset meteorologi, sehingga diperlukan algoritma klasifikasi yang mampu menangani nilai atribut yang tidak lengkap dalam data meteorologi. Dataset meteorologi yang digunakan terdiri dari atribut temperatur, kelembaban, tekanan udara, kecepatan angin, dan curah hujan. Pada penelitian ini, penanganan data yang tidak lengkap menggunakan algoritma klasifikasi berbasis rough set dan fuzzy rough set. Hasil yang diperoleh berupa rules untuk mengklasifikasikan data meteorologi tidak lengkap pada data uji. Hasil pengujian algoritma rough set dan fuzzy rough set pada dataset yang memuat 5%, 10%, 15%, 20%, 25%, dan 30% missing value menunjukkan bahwa: (i) akurasi rules berbasis algoritma rough set mengalami penurunan ketika persentase missing value bertambah, sedangkan akurasi rules berbasis algoritma fuzzy rough set mengalami peningkatan ketika persentase missing value ditingkatkan sampai 25% dan akurasi mengalami penurunan ketika persentase missing value bertambah menjadi 30%, (ii) peningkatan persentase missing value mempengaruhi jumlah rules dan waktu komputasi pembentukan rules berbasis algoritma rough set, tetapi tidak berpengaruh pada jumlah rules dan waktu komputasi pembentukan rules berbasis algoritma fuzzy rough set, dan (iii) pada penerapan rules terhadap data uji, terdapat data uji yang tidak dapat diprediksi oleh rules berbasis algoritma rough set, tetapi dapat diprediksi oleh rules berbasis algoritma fuzzy rough set. ========== Data mining is a process of finding patterns and knowledge of the database. Most of the databases in the real world can not be avoided from the problem of incompleteness. This is caused partly by faulty procedure manual data entry, wrong measurements, and equipment faults. One of database that can not be separated from the problem of incompleteness is the meteorological dataset, so that required classification algorithm that capable of handling incomplete attribute values in meteorological data. Meteorological dataset is used consist of the average temperature, humidity, air pressure, wind of speed, and rainfall. In this study, the handling of incomplete data use classification algorithm based on rough sets and fuzzy rough sets. The results obtained in the form of rules for classifying the incomplete meteorological data on test data. Results of the testing rough set and fuzzy rough set algorithm on meteorological dataset containing 5%, 10%, 15%, 20%, 25%, and 30% missing value showed that: (i) the accuracy of the rules based rough set algorithm decreased when the percentage of missing value increases, while the accuracy of the rules based fuzzy rough set algorithm increased when the percentage of missing value increased to 25% and accuracy decreased when the percentage of missing value increased to 30%, (ii) an increase in the percentage of missing value affects the number of rules and computing time of forming rules based rough sets algorithm, but it had no effect on the number of rules and computing time of forming rules based fuzzy rough sets algorithms, and (iii) application of rules for the test data, there is a test data that can not be predicted by the rules based rough sets algorithm, but can be predicted by rules based fuzzy rough sets algorithms