Revealing Driver’s Natural Behavior—A GUHA Data Mining Approach

We investigate the applicability and usefulness of the GUHA data mining method and its computer implementation LISp-Miner for driver characterization based on digital vehicle data on gas pedal position, vehicle speed, and others. Three analytical questions are assessed: (1) Which measured features, also called attributes, distinguish each driver from all other drivers? (2) Comparing one driver separately in pairs with each of the other drivers, which are the most distinguishing attributes? (3) Comparing one driver separately in pairs with each of the other drivers, which attributes values show significant differences between drivers? The analyzed data consist of 94,380 measurements and contain clear and understandable patterns to be found by LISp-Miner. In conclusion, we find that the GUHA method is well suited for such tasks.

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PENAMBANGAN DATA FORMAT TEXT EXCEL DENGAN SOFTWARE TANAGRA

Elektron : Jurnal Ilmiah ◽

10.30630/eji.1.1.6 ◽

2009 ◽

Vol 1 (1) ◽

pp. 20-28

Author(s):

Julsam Julsam ◽

Handryawan Adnan Mooduto

Keyword(s):

Data Mining ◽

Data Type ◽

The Other ◽

Mining Method ◽

Text Data ◽

Data Format ◽

Software Application ◽

Data Mining Method ◽

Other Information ◽

Existing Data

This article describes about Tanagra software application on data mining. Tanagra is data mining software which can be used to access some existing data mining method. Data is built using excel with text data type. This application use the dataset of women with begnin or malignant. The result inform from 699 sample for Univariate Continuous Statistic have begin 65,52% and malignant 34,48%. The other information is the begnin has less mitos (1,6 : 1,59), the malignant has more ucellshape (6,56 : 3,21)

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PENGELOMPOKKAN PADA KENDARAAN BERMOTOR MENURUT KEGUNAANNYA MENGGUNAKAN METODE DATA MINING K-MEANS

KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) ◽

10.30865/komik.v3i1.1687 ◽

2019 ◽

Vol 3 (1) ◽

Author(s):

Windayani Pulungan ◽

Poningsih Poningsih ◽

Heru Satria

Keyword(s):

Data Mining ◽

Public Transportation ◽

Motor Vehicles ◽

Mining Method ◽

Data Mining Method ◽

Vehicle Data ◽

Private Transportation ◽

Motorized Vehicles ◽

Different Characteristics ◽

Existing Data

This study aims to look for the grouping of data in motor vehicles according to their use. The types of motorized vehicles in this study are ranging from motorbikes, cars, public transportation, taxis, public transportation, buses, and pick ups. In this case we need a method that can classify vehicle data according to its use. This research was conducted in Pematangsiantar and used the K-Means Data Mining method. K-Means method tries to group existing data into several groups, where data in one group has the same characteristics with each other and has different characteristics from the data in other groups. The highest cluster with the number of motorized vehicle data according to its use is 7 vehicles, namely, 3 wheels, Taxi, public transportation, Bus (public transportation), Truck / Pick Up (public transportation), Car, Truck / Pick Up (private transportation).Keywords: Data Mining, K-Means Method, Grouping of Motorized Vehicles.

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