Knowledge Discovery in Databases (K

Knowledge Discovery in Databases (KDD) covers various processes of exploring useful information from voluminous data. These data may contain several inconsistencies, missing records or irrelevant features, which make the knowledge extraction, a difficult process. So, it is essential to apply pre-processing techniques to these data in order to enhance its quality. Detailed description of data cleaning, imbalanced data handling and dimensionality reduction pre-processing techniques are depicted in this paper. Another important aspect of Knowledge Discovery is to filter, integrate, visualize and evaluate the extracted knowledge. In this paper, several visualization techniques such as scatter plots, parallel co-ordinates and pixel oriented technique are explained. The paper also includes detail descriptions of three visualization tools which are DBMiner, Spotfire and WinViz along with their comparative evaluation on the basis of certain criteria. It also highlights the research opportunities and challenges of Knowledge Discovery process.

Quality of data plays important role in information -oriented organizations, where the knowledge is extracted from data. Consistency, completeness, accuracy, validity and timeliness are the important characteristics of quality data.So, i t is important to obtain quality data for knowledge extraction. Data Cleaning is an important step of KDD process in order to recognize any inconsistency and incompleteness in the dataset and to improve its quality.
Dirty data leads to poor interpretation of knowledge. So, Data Cleaning is required to maintain Data Warehousing and deals with identifying and deleting errors and inconsistencies from data to enhance its quality. Several Data Cleaning techniques for handling missing attributes and noisy data are explained as follows:

Results (Indonesian) 1:

Knowledge Discovery in Databases (KDD) covers various processes of exploring useful information from voluminous data. These data may contain several inconsistencies, missing records or irrelevant features, which make the knowledge extraction, a difficult process. So, it is essential to apply pre-processing techniques to these data in order to enhance its quality. Detailed description of data cleaning, imbalanced data handling and dimensionality reduction pre-processing techniques are depicted in this paper. Another important aspect of Knowledge Discovery is to filter, integrate, visualize and evaluate the extracted knowledge. In this paper, several visualization techniques such as scatter plots, parallel co-ordinates and pixel oriented technique are explained. The paper also includes detail descriptions of three visualization tools which are DBMiner, Spotfire and WinViz along with their comparative evaluation on the basis of certain criteria. It also highlights the research opportunities and challenges of Knowledge Discovery process.Quality of data plays important role in information -oriented organizations, where the knowledge is extracted from data. Consistency, completeness, accuracy, validity and timeliness are the important characteristics of quality data.So, i t is important to obtain quality data for knowledge extraction. Data Cleaning is an important step of KDD process in order to recognize any inconsistency and incompleteness in the dataset and to improve its quality.Kotor data mengarah ke miskin interpretasi pengetahuan. Jadi, Data Cleaning diperlukan untuk mempertahankan Data pergudangan dan berkaitan dengan mengidentifikasi dan menghapus kesalahan dan inkonsistensi dari data untuk meningkatkan kualitasnya. Beberapa Data Cleaning teknik untuk menangani atribut hilang dan bising data tersebut dijelaskan sebagai berikut:

Results (Indonesian) 2:

Knowledge Discovery di Database (KDD) meliputi berbagai proses mengeksplorasi informasi yang berguna dari data tebal. Data ini mungkin berisi beberapa inkonsistensi, catatan hilang atau fitur yang tidak relevan, yang membuat ekstraksi pengetahuan, proses yang sulit. Jadi, adalah penting untuk menerapkan teknik pra-pengolahan data ini dalam rangka meningkatkan kualitasnya. Penjelasan rinci data pembersihan, penanganan data seimbang dan pengurangan dimensi teknik pra-pengolahan digambarkan dalam makalah ini. Aspek penting lain dari Knowledge Discovery adalah untuk menyaring, mengintegrasikan, memvisualisasikan dan mengevaluasi pengetahuan diekstrak. Dalam tulisan ini, beberapa teknik visualisasi seperti plot pencar, paralel koordinat dan teknik berorientasi pixel dijelaskan. Makalah ini juga termasuk detil deskripsi dari tiga alat visualisasi yang DBMiner, Spotfire dan WinViz bersama dengan evaluasi komparatif mereka atas dasar kriteria tertentu. Hal ini juga menyoroti peluang penelitian dan tantangan dari proses Knowledge Discovery. Kualitas data memainkan peran penting dalam informasi organisasi berorientasi, di mana pengetahuan diekstrak dari data. Konsistensi, kelengkapan, akurasi, validitas dan ketepatan waktu adalah karakteristik penting dari kualitas data.So, penting untuk mendapatkan data berkualitas untuk ekstraksi pengetahuan. Data Membersihkan merupakan langkah penting dari proses KDD untuk mengenali setiap inkonsistensi dan ketidaklengkapan dalam dataset dan untuk meningkatkan kualitas. Data Kotor menyebabkan interpretasi miskin pengetahuan. Jadi, Data Cleaning diperlukan untuk menjaga Data Warehousing dan penawaran dengan mengidentifikasi dan menghapus kesalahan dan inkonsistensi dari data untuk meningkatkan kualitasnya. Beberapa teknik data Cleaning untuk menangani atribut hilang dan data bising dijelaskan sebagai berikut: