Opportunity map: identifying causes of failure - a deployed data mining system
In this paper, we report a deployed data mining application system for Motorola. Originally, its intended use was for identifying causes of cellular phone failures, but it has been found to be useful for many other engineering data sets as well. For this report, the case study is a dataset containing cellular phone call records. This data set is like any dataset used in classification applications, i.e., with a set of attributes which can be continuous or discrete, and a discrete class attribute. In our application, the classes are normally ended calls, calls which failed to setup, and calls which failed while in progress. However, the task is not to predict any failure, but to identify possible causes that resulted in failures. Then, engineering efforts may focus on improvements that can be made to the phones. In the course of the project, various classification techniques, e.g., decision trees, naïve Bayesian classification and SVM were tried. However, the results were unsatisfactory. After several demonstrations and interaction with domain experts, we finally designed and implemented an effective approach to perform the task. The final system is based on class association rules, general impressions and visualization. The system has been deployed and is in regular use at Motorola. In this paper, we first describe our experiences with some existing classification systems and discuss why they are not suitable for the task. We then present our techniques. As an illustration, we show several visualization screens in the case study, which reveal some important knowledge. Due to confidentiality, we will not give specifics but only present a general discussion about the results.

title: Entity discovery and assignment for opinion mining applications
abstract: Opinion mining became an important topic of study in recent years due to its wide range of applications. There are also many companies offering opinion mining services. One problem that has not been studied so far is the assignment of entities that have been talked about in each sentence. Let us use forum discussions about products as an example to make the problem concrete. In a typical discussion post, the author may give opinions on multiple products and also compare them. The issue is how to detect what products have been talked about in each sentence. If the sentence contains the product names, they need to be identified. We call this problem entity discovery. If the product names are not explicitly mentioned in the sentence but are implied due to the use of pronouns and language conventions, we need to infer the products. We call this problem entity assignment. These problems are important because without knowing what products each sentence talks about the opinion mined from the sentence is of little use. In this paper, we study these problems and propose two effective methods to solve the problems. Entity discovery is based on pattern discovery and entity assignment is based on mining of comparative sentences. Experimental results using a large number of forum posts demonstrate the effectiveness of the technique. Our system has also been successfully tested in a commercial setting.

title: Opinion Mining
abstract: Описанл процедуру формування інтегрального показника об’єкта згідно з відгуками користувачів під час застосування методів оцінювання опінії текстової інформації у web-документах. Запропоновано використання лінгвістичних змінних та застосування вагових коефіцієнтів для достовірнішого результату оцінювання емоційного забарвлення текстової інформації. Ключові слова: опінія, емоційне забарвлення, об’єкт, інтегральний показник, лінгвістична змінна, ваговий коефіцієнт.

title: Dealing with different distributions in learning from
abstract: In the problem of learning with positive and unlabeled examples, existing research all assumes that positive examples P and the hidden positive examples in the unlabeled set U are generated from the same distribution. This assumption may be violated in practice. In such cases, existing methods perform poorly. This paper proposes a novel technique A-EM to deal with the problem. Experimental results with product page classification demonstrate the effectiveness of the proposed technique.

title: Finding Actionable Knowledge via Automated Comparison
abstract: The problem of finding interesting and actionable patterns is a major challenge in data mining. It has been studied by many data mining researchers. The issue is that data mining algorithms often generate too many patterns, which make it very hard for the user to find those truly useful ones. Over the years many techniques have been proposed. However, few have made it to real-life applications. At the end of 2005, we built a data mining system for Motorola (called Opportunity Map) to enable the user to explore the space of a large number of rules in order to find actionable knowledge. The approach is based on the concept of rule cubes and operations on rule cubes. A rule cube is similar to a data cube, but stores rules. Since its deployment, some issues have also been identified during the regular use of the system in Motorola. One of the key issues is that although the operations on rule cubes are flexible, each operation is primitive and has to be initiated by the user. Finding a piece of actionable knowledge typically involves many operations and intense visual inspections, which are labor-intensive and time-consuming. From interactions with our users, we identified a generic problem that is crucial for finding actionable knowledge. The problem involves extensive comparison of sub-populations and identification of the cause of their differences. This paper first defines the problem and then proposes an effective method to solve the problem automatically. To the best of our knowledge, there is no reported study of this problem. The new method has been added to the Opportunity Map system and is now in daily use in Motorola.

title: Speed-up iterative frequent itemset mining with constraint changes
abstract: Mining of frequent itemsets is a fundamental data mining task. Past research has proposed many efficient algorithms for this purpose. Recent work also highlighted the importance of using constraints to focus the mining process to mine only those relevant itemsets. In practice, data mining is often an interactive and iterative process. The user typically changes constraints and runs the mining algorithm many times before being satisfied with the final results. This interactive process is very time consuming. Existing mining algorithms are unable to take advantage of this iterative process to use previous mining results to speed up the current mining process. This results in an enormous waste of time and computation. In this paper, we propose an efficient technique to utilize previous mining results to improve the efficiency of current mining when constraints are changed. We first introduce the concept of tree boundary to summarize useful information available from previous mining. We then show that the tree boundary provides an effective and efficient framework for the new mining. The proposed technique has been implemented in the context of two existing frequent itemset mining algorithms, FP-tree and tree projection. Experiment results on both synthetic and real-life datasets show that the proposed approach achieves a dramatic saving of computation.

title: A Population-Based Incremental Learning Algorithm with Elitist Strategy
abstract: The population-based incremental learning (PBIL) is a novel evolutionary algorithm combined the mechanisms of the Genetic Algorithm with competitive learning. In this paper, the influence of the number of selected best solutions on the convergence speed of the PBIL is studied by experiment. Based on experimental results, a PBIL algorithm with elitist strategy, named Double Learning PBIL (DLPBIL), is proposed. The new algorithm learns both the selected best solutions in current population and the optimal solution found so far in the algorithm at same time. Experimental results show that the DLPBIL out-performs the standard PBIL. Both the convergence speed and the solution quality are improved.

title: Filtering Spam in Social Tagging System with Dynamic Behavior Analysis
abstract: Spam in social tagging systems introduced by some malicious participants has become a serious problem for its global popularizing. Some studies which can be deduced to static user data analysis have been presented to combat tag spam, but either they do not give an exact evaluation or the algorithms’ performances are not good enough. In this paper, we proposed a novel method based on analysis of dynamic user behavior data for the notion that users’ behaviors in social tagging system can reflect the quality of tags more accurately. Through modeling the different categories of participants’ behaviors, we extract tag-associated actions which can be used to estimate whether tag is spam, and then present our algorithm that can filter the tag spam in the results of social search. The experiment results show that our method indeed outperforms the existing methods based on static data and effectively defends against the tag spam in various spam attacks.

title: Support Vector Machines for Text Categorization in Chinese Question Classification
abstract: Question classification plays a crucial important role in the question answering system because categorizing a given question is beneficial to identify an answer in the documents. The goal of question classification is to accurately assign labels to question based on expected answer type. Recently, many machine learning algorithms are used for question classification. However many research results show that SVM perform best in this task, because it is well known to work well for nonlinear, sparse, high dimensional problems. In this experiment, we perform the One-against-One SVM algorithm and a feature extraction method of Chinese questions to get high classification accuracy

title: Multi-agent System for Custom Relationship Management with SVMs Tool
abstract: Distributed data mining in the CRM is to learn available knowledge from the customer relationship so as to instruct the strategic behavior. In order to resolve the CRM in distributed data mining, this paper proposes the architecture of distributed data mining for CRM, and then utilizes the support vector machine tool to separate the customs into several classes and manage them. In the end, the practical experiments about one Chinese company are conducted to show the good performance of the proposed approach.

title: BitTube: Case Study of a Web-Based Peer-Assisted Video-on-Demand System
abstract: Recent theoretical and simulation-based studies have confirmed the tremendous benefit of peer-to-peer (P2P)communication at reducing the cost of running a VoD service.To date, very limited effort has been paid to validate the concept of peer-assisted VoD service, especially in terms of system implementation and service deployment. In this paper, we present the case study of a peer-assisted video-on-demand (VoD) system. We designed and developed Bit-Tube, a BitTorrent-compliant VoD system. By combining client/server and P2P downloading, it supports seamless transition across the spectrum from pure client-server mode to BitTorrent mode. Within this framework, we experiment with a series of piece picking policies to enhance BitTubepsilas support to video streaming and promote locality-aware P2P downloading. We evaluate our system over PlanetLab,which hosts the user-side component of the BitTube system and emulates the global-scale user requests to the VoD service.