Domestic Support for the U.S. Rice Sector and the WTO: Implications of the 2002 Farm Act

The U.S. rice sector is expected to receive some of the largest relative support under the 2002 Farm Act. USDA's rice baseline model is used to compute marketing loan benefits, while direct payments and counter-cyclical payments are estimated from endogenous prices and exogenous policy parameters. Alternative scenarios of reduced marketing loan benefits suggest that projected annual average sector revenue could decline by 4 to 27 percent.Agricultural and Food Policy,

ALGORITMA APRIORI POLA PENJUALAN PRODUK PADA TOKO MBAYEM

Bulan Ramadan menjadi salah satu bulan yang padat aktivitas khususnya bagi para pedagang. Hal ini tentu saja menjadi perhatian khusus bagi para pedagang terutama salah satu kios yang menjual sayur-mayur dan buah-buahan, yaitu Toko Mbayem. Pemilik Toko Mbayem mengerahkan perhatian khusus untuk strategi penjualan produk selama bulan Ramadan berlangsung. Salah satu strategi penjualan yang diterapkan Toko Mbayem adalah menentukan pola penjualan produk dengan cara membuat paket berisi produk-produk paling laris diminati dengan harga lebih murah. Namun karena penentuan pola penjualan produk masih menggunakan prakira pribadi pemilik Toko Mbayem, pola penentuan produk tidaklah akurat dan valid, sehingga perlu dirancang dan diimplementasikan untuk menentukan pola penjualan produk Toko Mbayem menggunakan algoritma Apriori. Data yang digunakan dengan cara memperoleh informasi penjualan produk yang merupakan data transaksi selama bulan Ramadan tahun 2022, karena pada periode bulan Ramadan aktivitas penjualan cenderung meningkat dengan pesat. Metode yang digunakan pada perancangan dan implementasi adalah metode Software Development Life Cycle dengan model Waterfall yang meliputi lima tahapan, diantaranya analisis, perancangan, implementasi, pengujian dan pemeliharaan. Hasil akhir pengolahan data dengan algoritma Apriori menyatakan bahwa dari 192 transaksi yang mengandung item1, Item2 dan item3, hanya 2 transaksi yang memenuhi nilai minimum relative support sebesar 30%, yaitu Cabai Rawit Merah, Bawang Merah dan Bumbu Dapur dengan frekuensi 96 dan nilai Relative Support 30,77%, sedangkan Cabai Rawit Merah, Bawang Merah dan Tomat Merah dengan frekuensi 99 dan nilai Relative Support 31,73%. Kebutuhan pada aplikasi dianalisis untuk selanjutnya dirancang sesuai alur kerja proses algoritma Apriori. Aplikasi diuji kepada target pengguna yang merupakan pemilik Toko Mbayem, kemudian dilakukan pemeliharaan guna menjaga fungsionalitas aplikasi. Hasil penelitian merupakan aplikasi pola penjualan produk Toko Mbayem yang mengidentifikasi keterkaitan antar produk serta menginterpretasi hasil identifikasi tersebut. Berdasarkan hasil kuesioner yang diberikan kepada 3 pengguna, pengguna menilai performa aplikasi sebesar 76% yang berarti bahwa aplikasi algoritma Apriori pada pola penjualan produk pada Toko Mbayem dinilai baik oleh penggun

Non-redundant rare itemset generation

Rare itemsets are likely to be of great interest because they often relate to high-impact transactions which may give rise to rules of great practical signi cance. Research into the rare association rule mining problem has gained momentum in the recent past. In this paper, we propose a novel approach that captures such rare rules while ensuring that redundant rules are eliminated. Extensive testing on real-world datasets from the UCI repository con rm that our approach outperforms both the Apriori-Inverse(Koh et al. 2006) and Relative Support (Yun et al. 2003) algorithms

An efficient closed frequent itemset miner for the MOA stream mining system

Mining itemsets is a central task in data mining, both in the batch and the streaming paradigms. While robust, efficient, and well-tested implementations exist for batch mining, hardly any publicly available equivalent exists for the streaming scenario. The lack of an efficient, usable tool for the task hinders its use by practitioners and makes it difficult to assess new research in the area. To alleviate this situation, we review the algorithms described in the literature, and implement and evaluate the IncMine algorithm by Cheng, Ke, and Ng (2008) for mining frequent closed itemsets from data streams. Our implementation works on top of the MOA (Massive Online Analysis) stream mining framework to ease its use and integration with other stream mining tasks. We provide a PAC-style rigorous analysis of the quality of the output of IncMine as a function of its parameters; this type of analysis is rare in pattern mining algorithms. As a by-product, the analysis shows how one of the user-provided parameters in the original description can be removed entirely while retaining the performance guarantees. Finally, we experimentally confirm both on synthetic and real data the excellent performance of the algorithm, as reported in the original paper, and its ability to handle concept drift.Postprint (published version

Evolving temporal association rules with genetic algorithms

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty

A pattern mining approach for information filtering systems

It is a big challenge to clearly identify the boundary between positive and negative streams for information filtering systems. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on the RCV1 data collection, and substantial experiments show that the proposed approach achieves encouraging performance and the performance is also consistent for adaptive filtering as well

Problem-Solving Knowledge Mining from Users’\ud Actions in an Intelligent Tutoring System

In an intelligent tutoring system (ITS), the domain expert should provide\ud relevant domain knowledge to the tutor so that it will be able to guide the\ud learner during problem solving. However, in several domains, this knowledge is\ud not predetermined and should be captured or learned from expert users as well as\ud intermediate and novice users. Our hypothesis is that, knowledge discovery (KD)\ud techniques can help to build this domain intelligence in ITS. This paper proposes\ud a framework to capture problem-solving knowledge using a promising approach\ud of data and knowledge discovery based on a combination of sequential pattern\ud mining and association rules discovery techniques. The framework has been implemented\ud and is used to discover new meta knowledge and rules in a given domain\ud which then extend domain knowledge and serve as problem space allowing\ud the intelligent tutoring system to guide learners in problem-solving situations.\ud Preliminary experiments have been conducted using the framework as an alternative\ud to a path-planning problem solver in CanadarmTutor