Jawaban UAS Data Mining - Venny Tanawi

Jawaban terbaik ujian akhir data mining (LB11) adalah dari venny tanawy. Jawaban terbaik lainnya adalah dari Ria Liuswani, Yulia Agustina, dan Hidayat Sadikin. Berikut adalah jawaban dari Venny Tanawi:

1

2 (ada kesalahan kecil untuk C2 (20+30+25)/3 seharusnya = 25. Tetapi hasil kesalahan kecil ini tidak masalah karena yang terpenting adalah pemahaman yang ada dalam proses pengerjaan)

3

4

Data Mining Trends and Research Frontiers - Kelompok Bo Cua Gpp

Kelompok:
Ria Liuswani  1501144950
Ariandika   1501144206
Shinta  1501147574
Trias Tanujaya   1501151432
Johan Iswara   1501151060
Denny Putra    1501155784

Hierarchical Clustering - kelompok Otaku

Data Mining - Clustering: K-Medoid oleh Kelompok Selasa

Tugas kelompok data mining k-means

The Jambi:
- 1501170803 DIDIEK PUTRA OETOMO
- 1501178503 RATNA SARI
- 1501177702 VENNY TANAWI
- 1501185194 RYAN DARMASAPUTRA
- 1501191120 RAYMOND BINTANG
- 1501183623 WILLIAM *)

9 Des 2014 - Kelompok Jambi - K-Means

GSLC

Berikut adalah online quiz pengisi GSLC minggu ini untuk kelas data mining (LB11):

Ceritakan/uraikan termasuk menggunakan contoh apa yang dimaksud dengan constraint berikut dalam data mining: anti-monotonicity constraint, monotonicity constraint, succint constraint, dan covertible constraint! (meeting ke-8/GSLC)

Jawaban dalam bentuk cerita/uraian dimaksudkan untuk menghindari jawaban yang 'copy-paste'
Jawaban yang baik dan benar akan mendapatkan poin reward untuk UAS 10 poin (khusus untuk uraian yang 'outstanding' akan mendapat 15 poin).
Jawaban/cerita/uraian yang copy-paste tidak akan mendapatkan poin reward.
Deadline sampai dengan minggu 7-12-2014.
Silahkan submit melaui email di avds168@gmail.com

selamat mengerjakan

regards

Soal & Jawaban UTS Data Mining

Essay:

1. Apa perbedaan supervised learning dan unsupervised learning? 

  

2. Berikut adalah decision tree

Suatu object X memiliki nilai-nilai atribut seperti berikut, X=(A=100; B=0; C=20). Klasifikasikan kelas apakah object X? 

Processes in Data Mining

Processes in Data Mining

To carry out projects in Data Mining systematically, a process that generally applies is usually applied. Based on 'best practice', practitioners and researchers DM proposes several processes (workflows or approach step-by-step simple) to increase the chances of success in implementing projects DM. Efforts that eventually resulted in several processes that serve as standards, some of which (the most popular) are discussed in this section.

Myths and Blunders in Data Mining

Data mining is a great analitics tool. Data mining helps many manager to see customer behaviour. Result in data mining is to increase revenue, to decrease production cost, and to discover fraud. Data mining is usually linked into many myths, below are some of them.

• Data mining give instant results, this is not true because data mining is a step by step that need many consideration.
• Not yet ready for business, this is the best to implement in business environment.
• Need a separated  database, data mining can use available data base.
• only those with high technology that only can use it.
• only for big company

Real Life Implementation of Data Mining

Real Life Implementation of Data Mining 

From the examples stated in the article in this link about "Real Life Implementation of Data Mining", the conclusions are pretty much simple.

To begin with, i think all of us should first take an understanding about the definition of this so called "Data Mining" itself. Data Mining is a computational process of digging through a collection of data to find unique patterns and behaviours by using mathematical formula in order to help users to make a future prediction about what's going to happen to their organization in the near future. To simplify this, Data Mining is a computational process of finding predictions about what's going to happen in the future, like a psychic.

How Data Mining Works

Data mining creates few model to identify patterns on attributes which is inscribed on dataset. Some of those patterns will create  “descriptive” results, whereas some pattern creates “Predictive” results.

Generally, data mining identifies four kind of main patterns: 

• Associations : groups same or similar events, for example a purchase of same item at supermarket.
• Predictions : forecast future events based on historical record. For example: predict future world cup winner based on winning streak pattern.
• Clusters :  groups objects based on known characters, for example create a customer differentiation based on purchase history.
• Sequential relationships : find a relation between events, for example :  predict a customer whom purchased a car will purchase spare parts a year later.

These general patterns are extracted from data manually for hundred years, but increased data volume on modern times demands  an automated approach. The automated (or semi – automated) approach which analyzes very large amount of dataset is called Data Mining.

Methods in Data Mining

Classification

In data mining classification perhaps considered being the most used method for problem solving. Classification studies the patterns of historical data (a set of information - like characteristics, variables, features - on a variety of characteristics of the items that have been labeled previously) for the purpose of placing new instances (objects) into groups or classes. For example classifications can be used to predict weathers, frauds, communications, and other class labeled conditions. However when the condition type to be predicted is in a numerical data it cannot be called classifications instead of regression.

Various Data Mining Tools

Examples of data mining tools are SPSS (PASW Modeler, formerly known as Clementine), SAS (Enterprise Miner), StstSoft (Statistica Data Miner), Salford (CART, MARS, TreeNet, RandomForest), Angoss (KnowledgesSTUDIO, KnowledgeSeeker), and Megaputer (PolyAnalyst).

BI tools such as IBM Cognos, Oracle Hyperion, SAP Business Objects, Microstrategy, Teradata, and Microsoft only focused on multi-dimensional modeling and data visualization and not intended to be competitor of data mining tools.

Data Perprocessing - Online Quiz Data Mining - by Ria Liuswani - 1501144950

Tahap awal dan sekaligus tahap yang paling penting dalam data mining adalah ‘data preprocessing’. Apa saja bentuk atau teknik dalam data preprocessing?

by Ria Liuswani  - 1501144950

Pre processing dapat dilakukan dengan beberapa teknik yaitu:

Data Mining for Business Intelligence - Application and Data Mining Concept

Application and Data Mining Concept

Definition of Data mining in BI is a step for developing businesses that created by gathering the data, organize, and save them in the organization. The techniques of data mining is very global and itself get used widely by many companies.

The first component of the data mining is begin with analyzing the total of the data that much more to gathered inside the company. 

Apriori Algorithm By : The Zawas

Mining Frequent Patterns And Associations - Kelompok NOB

Jenis-jenis Atribut Data dalam Data Mining

Apa yang dimaksud dengan atribut?

Atribut adalah bagian data, yang mewakili karakteristik atau feature dari objek data. Atribut, dimensi, feature, dan variabel sering digunakan secara bergantian dalam literatur. Istilah dimensi ini umumnya digunakan dalam literatur datawarehouse. Dalam literatur Machine learning cenderung menggunakan istilah feature, sementara statistik lebih menggunakan istilah variabel. 

Data mining dan para profesional database biasanya menggunakan istilah atribut, dan disini akan kita gunakan istilah atribut juga. 

Online Quiz: Decision Tree - Rules Based - Naive Bayesian Classification

Di bawah ini adalah tabel yang berisi training data dari database karyawan.  Data ini sudah di-geralisasi-kan. Misalnya, “31…35” untuk usia berarti kisaran usia antara 31 hingga 35. Khusus untuk kolom  "jumlah", itu menunjukkan  jumlah tuples dari masing-masing data pada department, status, usia, dan gaji pada baris yang dimaksud

Data Mining Assignment: Bayes Classification Method & Model Evaluation, By Muchas Gracias