Crime Pattern Detection Using Data Mining

Can crimes be modeled as data mining problems? We will try to answer this question in this paper. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. Here we look at use of clustering algorithm for a data mining approach to help detect the crimes patterns and speed up the process of solving crime. We will look at k-means clustering with some enhancements to aid in the process of identification of crime patterns. We will apply these techniques to real crime data from a sheriff’s office and validate our results. We also use semi-supervised learning technique here for knowledge discovery from the crime records and to help increase the predictive accuracy. We also developed a weighting scheme for attributes here to deal with limitations of various out of the box clustering tools and techniques. This easy to implement machine learning framework works with the geo-spatial plot of crime and helps to improve the productivity of the detectives and other law enforcement officers. It can also be applied for counter terrorism for homeland security

The Work-Product Doctrine: Protection, Not Privilege

Although the work-product doctrine has received considerable attention before the courts in recent years, several issues regarding the scope and applicability of the doctrine remain controversial As a prelude to explaining the state of the law on these issues, the author examines the case law through which the doctrine developed and explores the doctrine\u27s modern application through rule 26 of the Federal Rules of Civil Procedure. He next discusses the rule\u27s various requirements and its treatment ofparticular categories of information including opinion work product andparty statements. Finally, Professor Cohn explains how the rule\u27s protection may be waived and discusses the rule\u27s operation with respect to subsequent litigation and aparty\u27s use of experts. The author draws distinctions throughout the article between operation of the attorney- client privilege and the work-product doctrine and concludes that the work-product doctrine operates not as aprivilege that belongs to any party but rather as a protection for the adversary systetr

Mining web data for competency management

We present CORDER (COmmunity Relation Discovery by named Entity Recognition) an un-supervised machine learning algorithm that exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments

Computational Models (of Narrative) for Literary Studies

In the last decades a growing body of literature in Artificial Intelligence (AI) and Cognitive Science (CS) has approached the problem of narrative understanding by means of computational systems. Narrative, in fact, is an ubiquitous element in our everyday activity and the ability to generate and understand stories, and their structures, is a crucial cue of our intelligence. However, despite the fact that - from an historical standpoint - narrative (and narrative structures) have been an important topic of investigation in both these areas, a more comprehensive approach coupling them with narratology, digital humanities and literary studies was still lacking. With the aim of covering this empty space, in the last years, a multidisciplinary effort has been made in order to create an international meeting open to computer scientist, psychologists, digital humanists, linguists, narratologists etc.. This event has been named CMN (for Computational Models of Narrative) and was launched in the 2009 by the MIT scholars Mark A. Finlayson and Patrick H. Winston1

Rule 26(a)(2)(B) of the Federal Rules of Civil Procedure: In the Interest of Full Disclosure

This Note examines the varying interpretations of Rule 26(a)(2)(B) of the Federal Rules of Civil Procedure, an issue currently dividing the nation\u27s circuit courts of appeal and district courts. Interpreting the Rule for its plain meaning yields an exemption for expert witnesses who are either treating physicians or employees of a party in the case. While some courts have followed this textualist approach, more have opted for a broader interpretation, imposing the expert report requirements of Rule 26 on employee experts and treating physicians under certain circumstances. In keeping with the spirit of the Rules, courts should interpret the Rule broadly so as to encourage full disclosure while the Advisory Committee on the Federal Rules of Civil Procedure considers potential amendments

Rule 26(a)(2)(B) of the Federal Rules of Civil Procedure: In the Interest of Full Disclosure

This Note examines the varying interpretations of Rule 26(a)(2)(B) of the Federal Rules of Civil Procedure, an issue currently dividing the nation\u27s circuit courts of appeal and district courts. Interpreting the Rule for its plain meaning yields an exemption for expert witnesses who are either treating physicians or employees of a party in the case. While some courts have followed this textualist approach, more have opted for a broader interpretation, imposing the expert report requirements of Rule 26 on employee experts and treating physicians under certain circumstances. In keeping with the spirit of the Rules, courts should interpret the Rule broadly so as to encourage full disclosure while the Advisory Committee on the Federal Rules of Civil Procedure considers potential amendments

Detecting domestic violence.

Over 90% of the case data from police inquiries is stored as unstructured text in police databases. We use the combination of Formal Concept Analysis and Emergent Self Organizing Maps for exploring a dataset of unstructured police reports out of the Amsterdam-Amstelland police region in the Netherlands. In this paper, we specifically aim at making the reader familiar with how we used these two tools for browsing the dataset and how we discovered useful patterns for labelling cases as domestic or as non-domestic violence.Formal concept analysis (FCA); Emergent SOM; Domestic violence; Knowledge discovery in databases; Text mining; Exploratory data analysis;

Relation Discovery from Web Data for Competency Management

This paper describes a technique for automatically discovering associations between people and expertise from an analysis of very large data sources (including web pages, blogs and emails), using a family of algorithms that perform accurate named-entity recognition, assign different weights to terms according to an analysis of document structure, and access distances between terms in a document. My contribution is to add a social networking approach called BuddyFinder which relies on associations within a large enterprise-wide "buddy list" to help delimit the search space and also to provide a form of 'social triangulation' whereby the system can discover documents from your colleagues that contain pertinent information about you. This work has been influential in the information retrieval community generally, as it is the basis of a landmark system that achieved overall first place in every category in the Enterprise Search Track of TREC2006

Pushing Your Point of View: Behavioral Measures of Manipulation in Wikipedia

As a major source for information on virtually any topic, Wikipedia serves an important role in public dissemination and consumption of knowledge. As a result, it presents tremendous potential for people to promulgate their own points of view; such efforts may be more subtle than typical vandalism. In this paper, we introduce new behavioral metrics to quantify the level of controversy associated with a particular user: a Controversy Score (C-Score) based on the amount of attention the user focuses on controversial pages, and a Clustered Controversy Score (CC-Score) that also takes into account topical clustering. We show that both these measures are useful for identifying people who try to "push" their points of view, by showing that they are good predictors of which editors get blocked. The metrics can be used to triage potential POV pushers. We apply this idea to a dataset of users who requested promotion to administrator status and easily identify some editors who significantly changed their behavior upon becoming administrators. At the same time, such behavior is not rampant. Those who are promoted to administrator status tend to have more stable behavior than comparable groups of prolific editors. This suggests that the Adminship process works well, and that the Wikipedia community is not overwhelmed by users who become administrators to promote their own points of view